Statistics & Probability 2017 by Cambridge University Press

Statistics & Probability Books 2017

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Contents

Featured Highlights

Sir David Cox

Bradley Efron

Statistical Theory and Methods

Probability

Actuarial Science

Finance and Economics

Life sciences, Medicine and Health

Computer Science

Physical Sciences and Engineering

Social and Behavioural Sciences 17 Also of Interest

Information on related journals Inside back cover

ighlight

see page 1

see page 5

see page 6

see page 9

see page 14

Featured authors Sir David Cox Cambridge University Press published three books by Sir David Cox in recent years and their diversity illustrates the range of application of modern statistical ideas. The first, Principles of Statistical Inference (2006), deals with the role of probability in underpinning the assessment of uncertainty involved in interpreting data. The second, Principles of Applied Statistics (2011), co-authored by Christl Donnelly, discusses the application of statistical ideas to the design and analysis of investigations with illustrations from many fields. The third, Case-Control Studies (2014), co-authored by Ruth Keogh, examines a type of retrospective investigation of importance especially but not only in epidemiology. Previously in 2005, the Press published two volumes of his selected papers with commentaries by the author. Sir David Cox is the first recipient of the International Research Prize set up last year by five learned societies concerned with statistical applications and theory and also joint holder with Bradley Efron (Stanford), also a Cambridge University Press author, of the 2017 BBVA Foundation Frontiers of Knowledge Award in the Basic Sciences.

Bradley Efron and Trevor Hastie Authors of Computer Age Statistical Inference The prize-winning Computer Age Statistical Inference by two eminent scientists examines how the theory and practice of statistics has evolved from the introduction of electronic computation to our current big data environment. A series of important topics - empirical Bayes, survival analysis, the jackknife and bootstrap, random forests, deep learning, and dozens more - are examined, both for what they do and why they are done. The book is intended for both statisticians and other researchers with interests in data science. A score of data sets, small and large, (available from the authors’ book website: web. stanford.edu/~hastie/CASI/) are used as illustration, accompanied by more than two hundred displays.

René L. Schilling Author of Measures, Integrals and Martingales Author René L. Schilling sheds light on his book Measures, Integrals and Martingales: When writing Measures, Integrals and Martingales, I wanted to create a text which can be used in class and as a text for self-study for undergraduates. This meant that I deferred all topological aspects to later chapters, thus creating a text on measure and integration in a “familiar” Euclidean setting, but with proofs which are easily transferred to more abstract situations. It is a unique feature of the text that martingales and conditional expectations are not only developed for probability measures but for general sigma-finite measures.

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Featured Highlights

Featured Highlights Computer Age Statistical Inference Algorithms, Evidence, and Data Science Bradley Efron Stanford University, California

and Trevor Hastie Stanford University, California

Computing power has revolutionized the theory and practice of statistical inference. This book delivers a concentrated course in modern statistical thinking by tracking the revolution from classical theories to the large-scale prediction algorithms of today. Anyone who applies statistical methods to data will benefit from this landmark text. Contents: Part I. Classic Statistical Inference: 1. Algorithms and inference; 2. Frequentist inference; 3. Bayesian inference; 4. Fisherian inference and maximum likelihood estimation; 5. Parametric models and exponential families; Part II. Early Computer-Age Methods: 6. Empirical Bayes; 7. James–Stein estimation and ridge regression; 8. Generalized linear models and regression trees; 9. Survival analysis and the EM algorithm; 10. The jackknife and the bootstrap; 11. Bootstrap confidence intervals; 12. Cross-validation and Cp estimates of prediction error; 13. Objective Bayes inference and Markov chain Monte Carlo; 14. Statistical inference and methodology in the postwar era; Part III. Twenty-First Century Topics: 15. Large-scale hypothesis testing and false discovery rates; 16. Sparse modeling and the lasso; 17. Random forests and boosting; 18. Neural networks and deep learning; 19. Support-vector machines and kernel methods; 20. Inference after model selection; 21. Empirical Bayes estimation strategies; Epilogue; References; Index. ‘How and why is computational statistics taking over the world? In this serious work of synthesis that is also fun to read, Efron and Hastie, two pioneers in the integration of parametric and nonparametric statistical ideas, give their take on the unreasonable effectiveness of statistics and machine learning in the context of a series of clear, historically informed examples.’ Andrew Gelman, Columbia University, New York

power. The authors’ perspective is summarized nicely when they say, ‘very roughly speaking, algorithms are what statisticians do, while inference says why they do them’. The book explains this ‘why’; that is, it explains the purpose and progress of statistical research, through a close look at many major methods, methods the authors themselves have advanced and studied at great length. Both enjoyable and enlightening, Computer Age Statistical Inference is written especially for those who want to hear the big ideas, and see them instantiated through the essential mathematics that defines statistical analysis. It makes a great supplement to the traditional curricula for beginning graduate students.’ Rob Kass, Carnegie Mellon University, Pennsylvania

‘This is a terrific book. It gives a clear, accessible, and entertaining account of the interplay between theory and methodological development that has driven statistics in the computer age. The authors succeed brilliantly in locating contemporary algorithmic methodologies for analysis of ‘big data’ within the framework of established statistical theory.’ Alastair Young, Imperial College London

PROSE Award for Computing and Information Sciences 2017 – Winner Institute of Mathematical Statistics Monographs, 5

2016 228 x 152 mm 495pp 5 b/w illus. 40 colour illus. 50 tables 978-1-107-14989-2 Hardback £45.99 / US$74.99 For all formats available, see

www.cambridge.org/9781107149892

Random Graphs and Complex Networks Volume 1 Remco van der Hofstad Technische Universiteit Eindhoven, The Netherlands

Network science is one of the fastest growing areas in science and business. This classroom-tested, self-contained book is designed for master’s-level courses and provides a rigorous treatment of random graph models for networks, featuring many examples of real-world networks for motivation and numerous exercises to build intuition and experience. Contents: Preface; Course outline; 1. Introduction; Part I. Preliminaries: 2. Probabilistics methods; 3. Branching processes; Part II. Basic Models: 4. Phase transition for the Erdos–Renyi random graph; 5. Erdos–Renyi random graph

revisited; Part III. Models for Complex Networks: 6. Generalized random graphs; 7. Configuration model; 8. Preferential attachment models; Appendix; Glossary; References; Index. ‘… a modern and deep, yet accessible, introduction to the models that make up [the] basis for the theoretical study of random graphs and complex networks. The book strikes a balance between providing broad perspective and analytic rigor that is a pleasure for the reader.’ Adam Wierman, California Institute of Technology

‘This text builds a bridge between the mathematical world of random graphs and the real world of complex networks. It combines techniques from probability theory and combinatorics to analyze the structural properties of large random graphs. Accessible to network researchers from different disciplines, as well as masters and graduate students, the material is suitable for a one-semester course, and is laced with exercises that help the reader grasp the content. The exposition focuses on a number of core models that have driven recent progress in the field, including the Erdős–Rényi random graph, the configuration model, and preferential attachment models. A detailed description is given of all their key properties. This is supplemented with insightful remarks about properties of related models so that a full panorama unfolds. As the presentation develops, the link to complex networks provides constant motivation for the routes that are being chosen.’ Frank den Hollander, Universiteit Leiden

‘The first volume of Remco van der Hofstad’s Random Graphs and Complex Networks is the definitive introduction into the mathematical world of random networks. Written for students with only a modest background in probability theory, it provides plenty of motivation for the topic and introduces the essential tools of probability at a gentle pace. It covers the modern theory of Erdős–Rényi graphs, as well as the most important models of scalefree networks that have emerged in the last fifteen years. This is a truly wonderful first volume; the second volume, leading up to current research topics, is eagerly awaited.’ Peter Mörters, University of Bath Cambridge Series in Statistical and Probabilistic Mathematics, 43

2016 253 x 177 mm 236pp 58 b/w illus. 5 tables 978-1-107-17287-6 Hardback £39.99 / US$59.99 For all formats available, see

www.cambridge.org/9781107172876

eBooks available at www.cambridge.org/ebookstore

Featured Highlights

Probability on Trees and Networks Russell Lyons Indiana University, Bloomington

and Yuval Peres Microsoft Research, Washington

This authoritative state-of-the-art account of probability on networks for graduate students and researchers in mathematics, statistics, computer science, and engineering, brings together sixty years of research, including many developments where the authors played a leading role. The text emphasizes intuition, while also giving complete proofs. Contents: 1. Some highlights; 2. Random walks and electric networks; 3. Special networks; 4. Uniform spanning trees; 5. Branching processes, second moments, and percolation; 6. Isoperimetric inequalities; 7. Percolation on transitive graphs; 8. The mass-transport technique and percolation; 9. Infinite electrical networks and Dirichlet functions; 10. Uniform spanning forests; 11. Minimal spanning forests; 12. Limit theorems for Galton–Watson processes; 13. Escape rate of random walks and embeddings; 14. Random walks on groups and Poisson boundaries; 15. Hausdorff dimension; 16. Capacity and stochastic processes; 17. Random walks on Galton– Watson trees. ‘This long-awaited work focuses on one of the most interesting and important parts of probability theory. Half a century ago, most work on models such as random walks, Ising, percolation and interacting particle systems concentrated on processes defined on the d-dimensional Euclidean lattice. In the intervening years, interest has broadened dramatically to include processes on more general graphs, with trees being a particularly important case. This led to new problems and richer behavior, and as a result, to the development of new techniques. The authors are two of the major developers of this area; their expertise is evident throughout.’ Thomas M. Liggett, University of California, Los Angeles

‘Masterly, beautiful, encyclopaedic, and yet browsable – this great achievement is obligatory reading for anyone working near the conjunction of probability and network theory.’ Geoffrey Grimmett, University of Cambridge

‘For the last ten years, I have not let a doctoral student graduate without reading this [work]. Sadly, the earliest of those students are missing a considerable amount of material that the bound and published edition contains. Not only are the classical topics of random walks, electrical theory, and uniform spanning trees

covered in more coherent fashion than in any other source, but this book is also the best place to learn about a number of topics for which the other choices for textual material are limited. These include mass transport, random walk boundaries, and dimension and capacity in the context of Markov processes.’ Robin Pemantle, University of Pennsylvania Cambridge Series in Statistical and Probabilistic Mathematics, 42

2017 253 x 177 mm 716pp 78 b/w illus. 13 colour illus. 4 tables 864 exercises 978-1-107-16015-6 Hardback £49.99 / US$79.99 For all formats available, see

www.cambridge.org/9781107160156

recognized experts in the R language, so the reader attains the benefit of being taught by the ‘insiders’.’ Norm Matloff, University of California, Davis

‘This book is an excellent introduction to programming in R. It gently takes the reader from first principles in programming through to more advanced topics such as simulation and plotting. We recommend this book to our graduate students in computational biology as a concise guide to learning R.’ Stephen Eglen, University of Cambridge 2016 246 x 189 mm 230pp 40 b/w illus. 5 colour illus. 200 exercises 978-1-107-57646-9 Paperback £29.99 / US$44.99 For all formats available, see

A First Course in Statistical Programming with R

www.cambridge.org/9781107576469

Second edition W. John Braun

Data Management Essentials Using SAS and JMP

University of British Columbia, Okanagan

Julie Kezik

and Duncan J. Murdoch

Yale School of Public Health, Connecticut

University of Western Ontario

and Melissa Hill

A new edition of a bestselling text, this book provides a first course in programming for a broad range of students who need to work with data. Based on the open-source R statistical package, it introduces statistical graphics and numerical computing ideas such as simulation, optimization, and computational linear algebra.

Cd3 Inc., Austin, Texas

Contents: 1. Getting started; 2. Introduction to the R language; 3. Programming statistical graphics; 4. Programming with R; 5. Simulation; 6. Computational linear algebra; 7. Numerical optimization; Appendix. Review of random variables and distributions; Index. ‘For what has come to be called data analytics, R is a remarkable tour de force. Strong skills with R programming are needed to allow really effective use. Mastering the content of this carefully staged text is an excellent starting point for gaining those skills.’ John Maindonald, Australian National University, Canberra

‘This book should be especially useful for those without any prior programming background. The core programming material, such as loops and functions, is postponed to Chapter 4, allowing the student to first become comfortable with R in a broader manner. The placement of Chapter 3, on graphical methods, is particularly helpful in this regard, and is very motivating. The book is written by two

This book is designed for the first time or occasional SAS user who needs immediate guidance in navigating, exploring, visualizing, cleaning and reporting on data. It teaches the basic SAS skills essential to data management, including practical exercises with solutions. No formal or informal training is required. Contents: 1. Navigation; 2. Preliminary data exploration; 3. Storing and manipulating data; 4. Advanced concepts in dataset and variable manipulation; 5. Introduction to common procedures; 6. Procedures for simple statistics; 7. More about common procedures; 8. Data visualization; 9. JMP as an alternative. ‘The authors have created a very readable and gentle introduction to SAS programming and its working environment – Enterprise Guide. The text provides a valuable overview of ‘navigating’ in a SAS windowing environment and before moving quickly into core procedures. … a very valuable introduction to basic SAS programming for the beginning data analyst.’ Glenn Gamst, University of la Verne, California

‘The authors of Data Management Essentials Using SAS and JMP have written a thoroughly user-friendly beginning guide to SAS programming for data exploration and management, and for designing well-constructed and informative reports. I honestly know of no better book to use for self-instruction on, or for teaching

Featured Highlights / Sir David Cox essential SAS programming skills for report design than this very well written and produced book.’ Joseph M. Hilbe, Arizona State University 2016 234 x 177 mm 150pp 74 b/w illus. 4 tables 31 exercises 978-1-107-11456-2 Hardback £64.99 / US$99.99 978-1-107-53503-9 Paperback £19.99 / US$29.99 For all formats available, see

www.cambridge.org/9781107114562

Statistical Mechanics of Lattice Systems A Concrete Mathematical Introduction Sacha Friedli Universidade Federal de Minas Gerais, Brazil

and Yvan Velenik Université de Genève

Interest in network effects and phase changes in systems is bringing statistical mechanics to the heart of science and engineering. This rigorous introduction for advanced undergraduate or beginning graduate students in mathematics and physics teaches important concepts by studying specific models and includes many exercises, with solutions. Contents: Preface; Convention; 1. Introduction; 2. The Curie–Weiss model; 3. The Ising model; 4. Liquid-vapor equilibrium; 5. Cluster expansion; 6. Infinitevolume Gibbs measures; 7. Pirogov–Sinai theory; 8. The Gaussian free field on Zd; 9. Models with continuous symmetry; 10. Reflection positivity; A. Notes; B. Mathematical appendices; C. Solutions to exercises; Bibliography; Index. 2017 247 x 174 mm 600pp 978-1-107-18482-4 Hardback c. £44.99 / c. US$74.99 Publication October 2017 For all formats available, see

www.cambridge.org/9781107184824

highlight textbook

Network Science Albert-László Barabási Northeastern University, Boston

With Márton Pósfai

Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of disciplines from physics to the social sciences, is the only book needed for an introduction to network science. In modular format, with clear delineation between undergraduate and

graduate material, its unique design is supported by extensive online resources. Contents: Preface; Personal introduction; 1. Introduction; 2. Graph theory; 3. Random networks; 4. The scale-free property; 5. The Barabási–Albert model; 6. Evolving networks; 7. Degree correlation; 8. Network robustness; 9. Communities; 10. Spreading phenomena; Index. PROSE Award for Textbook/Best in Physical Sciences and Mathematics 2017 – Winner 2016 246 x 189 mm 475pp 371 colour illus. 12 tables 30 exercises 978-1-107-07626-6 Hardback £34.99 / US$59.99 For all formats available, see

www.cambridge.org/9781107076266

Sir David Cox Principles of Statistical Inference D. R. Cox Nuffield College, Oxford

2006 228 x 152 mm 236pp 978-0-521-86673-6 Hardback £72.00 / US$139.00 978-0-521-68567-2 Paperback £29.99 / US$56.00 For all formats available, see

www.cambridge.org/9780521866736

Principles of Applied Statistics D. R. Cox University of Oxford

and Christl A. Donnelly Imperial College London

This compact package delivers decades of usable scientific experience. Cox and Donnelly lay out the strategic thinking that characterises the successful application of statistics, and how this statistical strategy shapes every stage of an investigation. Essential reading for anyone who makes extensive use of statistical methods in their work. Contents: Preface; 1. Some general concepts; 2. Design of studies; 3. Special types of study; 4. Principles of measurement; 5. Preliminary analysis; 6. Model formulation; 7. Model choice; 8. Techniques of formal inference; 9. Interpretation; 10. Epilogue; References; Index.

No one is better placed than D. R. Cox to give the comprehensive, balanced account of the theory of statistical inference, its main ideas and controversies, that is now needed. This book is for every serious user or student of statistics – for anyone serious about the scientific understanding of uncertainty.

2011 228 x 152 mm 214pp 19 b/w illus. 978-1-107-01359-9 Hardback £67.00 / US$113.00 978-1-107-64445-8 Paperback £28.99 / US$45.99

Contents: Preface; 1. Preliminaries; 2. Some concepts and simple applications; 3. Significance tests; 4. More complicated situations; 5. Some interpretational issues; 6. Asymptotic theory; 7. Further aspects of maximum likelihood; 8. Additional objectives; 9. Randomization-based analysis; Appendix A. A brief history; Appendix B. A personal view; List of examples; References; Author index; Index.

Ruth H. Keogh

‘A deep and beautifully elegant overview of statistical inference, from one of the towering figures who created modern statistics. This book should be essential reading for all who call themselves ‘statisticians’.’ David Hand, Imperial College London

‘The explanations of key concepts are written so clearly … that they may be understood even if the mathematical details are skipped.’ MAA Online ‘The text is very well written and gives a balanced view of the frequentist and Bayesian notions of probability, without favouring one over the other.’ Journal of Applied Statistics

For all formats available, see

www.cambridge.org/9781107013599

Case-Control Studies London School of Hygiene and Tropical Medicine

and D. R. Cox University of Oxford

The case-control approach is a powerful method for investigating factors that may explain a particular event and is extensively used in epidemiology. With a particular focus on statistical analysis, this book is ideal for applied and theoretical statisticians wanting an up-to-date introduction to case-control studies. Contents: Preface; Preamble; 1. Introduction to case-control studies; 2. The simplest situation; 3. Matched case-control studies; 4. A general formulation; 5. Case-control studies with other than two outcomes; 6. Special sampling designs; 7. Nested case-control studies; 8. Case-subcohort studies; 9. Misclassification and measurement error; 10. Synthesis of studies; Appendix. A theoretical diversion; References; Index. ‘This book will rapidly become the bible for researchers using casecontrol studies. It covers essentially all aspects of such designs and their application.’ David J. Hand, Imperial College London

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Sir David Cox / Bradley Efron Institute of Mathematical Statistics Monographs, 4

2014 228 x 152 mm 293pp 30 b/w illus. 20 tables 978-1-107-01956-0 Hardback £62.00 / US$89.99 For all formats available, see

www.cambridge.org/9781107019560

Selected Statistical Papers of Sir David Cox Volume 1: Design of Investigations, Statistical Methods and Applications David Cox Nuffield College, Oxford

Edited by D. J. Hand Imperial College of Science, Technology and Medicine, London

and A. M. Herzberg Queen’s University, Ontario

Sir David Cox is one of the seminal statistical thinkers of the twentieth and twenty-first centuries. This selection, organised in two volumes, contains Professor Cox’s most important and interesting papers published before 1993. Each paper is the subject of a candid commentary by Professor Cox, written especially for this collection. Contents: Foreword; Preface; Part I. Design of Investigations: Design of experiments; Sampling; Part II. Statistical Methods: Point process data; Binary data; Survival data; Multivariate analysis; Miscellaneous; Part III. Applications. ‘The style is distinctive: the ideas are at once clearly expressed and at the same time deep and revealing to even experts. A student could do well to adopt these volumes as a major part of the graduate curriculum!’ Sankhya: The Indian Journal of Statistics ‘What makes this work of special value is David Cox’s willingness to contribute commentaries on the papers … I think many will be pleased to have these two volumes on their bookshelves.’ Publication of the International Statistical Institute ‘… David is a statistician’s statistician. It is too early to speak of these volumes as a fitting memorial but there is no doubt that they will stand when all our work has become part of that history which David has done so much to create.’ David Bartholomew, Sudbury 2006 247 x 174 mm 604pp 978-0-521-84939-5 Hardback £96.00 / US$152.00 For all formats available, see

www.cambridge.org/9780521849395

Selected Statistical Papers of Sir David Cox Volume 2: Foundations of Statistical Inference, Theoretical Statistics, Time Series and Stochastic Processes David Cox Nuffield College, Oxford

Edited by D. J. Hand Imperial College of Science, Technology and Medicine, London

and A. M. Herzberg Queen’s University, Ontario

www.cambridge.org/9780521849401

Selected Statistical Papers of Sir David Cox David Cox Nuffield College, Oxford

Edited by D. J. Hand Imperial College of Science, Technology and Medicine, London

and A. M. Herzberg Queen’s University, Ontario

Stochastic Processes: Foreword; Preface; Part IV. Foundations of Statistical Inference; Part V. Theoretical Statistics; Part VI. Time Series; Part VII. Stochastic Processes; Publications of Sir David Cox. ‘… any statistician … should be aware of the contents of the selected papers of Sir David Cox and he or she would certainly value their own copy.’ Kwantitatieve Methoden 2006 253 x 177 mm 1150pp 978-0-521-85816-8 2 Volume Hardback Set £309.00 / US$556.00 For all formats available, see

www.cambridge.org/9780521858168

Bradley Efron Computer Age Statistical Inference Algorithms, Evidence, and Data Science Bradley Efron Stanford University, California

and Trevor Hastie Stanford University, California

Bradley Efron / Statistical Theory and Methods 18. Neural networks and deep learning; 19. Support-vector machines and kernel methods; 20. Inference after model selection; 21. Empirical Bayes estimation strategies; Epilogue; References; Index. ‘How and why is computational statistics taking over the world? In this serious work of synthesis that is also fun to read, Efron and Hastie, two pioneers in the integration of parametric and nonparametric statistical ideas, give their take on the unreasonable effectiveness of statistics and machine learning in the context of a series of clear, historically informed examples.’ Andrew Gelman, Columbia University, New York

‘This unusual book describes the nature of statistics by displaying multiple examples of the way the field has evolved over the past sixty years, as it has adapted to the rapid increase in available computing power. The authors’ perspective is summarized nicely when they say, ‘very roughly speaking, algorithms are what statisticians do, while inference says why they do them’. The book explains this ‘why’; that is, it explains the purpose and progress of statistical research, through a close look at many major methods, methods the authors themselves have advanced and studied at great length. Both enjoyable and enlightening, Computer Age Statistical Inference is written especially for those who want to hear the big ideas, and see them instantiated through the essential mathematics that defines statistical analysis. It makes a great supplement to the traditional curricula for beginning graduate students.’ Rob Kass, Carnegie Mellon University, Pennsylvania

PROSE Award for Computing and Information Sciences 2017 – Winner Institute of Mathematical Statistics Monographs, 5

2016 228 x 152 mm 495pp 5 b/w illus. 40 colour illus. 50 tables 978-1-107-14989-2 Hardback £45.99 / US$74.99

New in Paperback

Large-Scale Inference Empirical Bayes Methods for Estimation, Testing, and Prediction Bradley Efron

Statistical Theory and Methods

Stanford University, California

Modern scientific technology, such as microarrays and fMRI machines, produces data in vast quantities. Bradley Efron explains the empirical Bayes methods that help make sense of a new statistical world. This is essential reading for professional statisticians and graduate students wishing to use and understand important new techniques like false discovery rates. Contents: Introduction and foreword; 1. Empirical Bayes and the James–Stein estimator; 2. Large-scale hypothesis testing; 3. Significance testing algorithms; 4. False discovery rate control; 5. Local false discovery rates; 6. Theoretical, permutation and empirical null distributions; 7. Estimation accuracy; 8. Correlation questions; 9. Sets of cases (enrichment); 10. Combination, relevance, and comparability; 11. Prediction and effect size estimation; A. Exponential families; B. Programs and data sets; Bibliography; Index. ‘In the last decade, Efron has played a leading role in laying down the foundations of large-scale inference, not only in bringing back and developing old ideas, but also linking them with more recent developments, including the theory of false discovery rates and Bayes methods. We are indebted to him for this timely, readable and highly informative monograph, a book he is uniquely qualified to write. It is a synthesis of many of Efron’s own contributions over the last decade with that of closely related material, together with some connecting theory, valuable comments, and challenges for the future. His avowed aim is ‘not to have the last word’ but to help us deal ‘with the burgeoning statistical problems of the twenty-first century’. He has succeeded admirably.’ Terry Speed, International Statistical Review Institute of Mathematical Statistics Monographs, 1

2012 228 x 152 mm 276pp 65 b/w illus. 10 colour illus. 105 exercises 978-1-107-61967-8 Paperback £28.99 / US$45.99 For all formats available, see

www.cambridge.org/9781107619678

Fundamentals of Nonparametric Bayesian Inference Subhashis Ghosal North Carolina State University

and Aad van der Vaart Universiteit Leiden

Written by top researchers, this selfcontained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses. Cambridge Series in Statistical and Probabilistic Mathematics, 44

2017 253 x 177 mm 656pp 15 b/w illus. 978-0-521-87826-5 Hardback £64.99 / US$89.99 Publication June 2017 For all formats available, see

www.cambridge.org/9780521878265

Handbook for Applied Modeling: Non-Gaussian and Correlated Data Jamie Riggs Northwestern University, Illinois

and Trent Lalonde University of Northern Colorado

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing data that fail idealized assumptions. It explains and demonstrates core techniques, common pitfalls, and data issues, and interpretation of model results, all with a focus on application, utility, and real-life data. 2017 253 x 177 mm 350pp 978-1-107-14699-0 Hardback £79.99 / US$99.99 978-1-316-60105-1 Paperback £29.99 / US$39.99 Publication July 2017 For all formats available, see

www.cambridge.org/9781107146990

For all formats available, see

www.cambridge.org/9781107149892

Visit our website at www.cambridge.org/statistics

Statistical Theory and Methods / Probability

Topics at the Frontier of Statistics and Network Analysis (Re)Visiting the Foundations Eric D. Kolaczyk Boston University

This snapshot of the current frontier of statistics and network analysis focuses on the foundational topics of modeling, sampling, and design. Primarily for graduate students and researchers in statistics and closely related fields, emphasis is not only on what has been done, but on what remains to be done. SemStat Elements

2017 228 x 152 mm 145pp 978-1-108-40712-0 Paperback £27.99 / US$34.99 Publication May 2017 For all formats available, see

www.cambridge.org/9781108407120

Graphical Models for Categorical Data Alberto Roverato Università di Bologna

For advanced students of network data science, this compact account covers both well-established methodology and the theory of models recently introduced in the graphical model literature. It focuses on the discrete case where all variables involved are categorical and, in this context, it achieves a unified presentation of classical and recent results. SemStat Elements

2017 228 x 152 mm 178pp 978-1-108-40496-9 Paperback £27.99 / US$34.99 Publication May 2017 For all formats available, see

www.cambridge.org/9781108404969

Mathematical Foundations of Infinite-Dimensional Statistical Models Evarist Giné University of Connecticut

and Richard Nickl University of Cambridge

High-dimensional and nonparametric statistical models are ubiquitous in modern data science. This book develops a mathematically coherent and objective approach to statistical inference in such models, with a focus on function estimation problems arising from random samples (density estimation) or

from Gaussian regression/signal in white noise problems. ‘Finally – a book that goes all the way in the mathematics of nonparametric statistics. It is reasonably selfcontained, despite its depth and breadth, including accessible overviews of the necessary analysis and approximation theory.’ Aad van der Vaart, Universiteit Leiden

PROSE Award for Mathematics 2017 – Winner Cambridge Series in Statistical and Probabilistic Mathematics, 40

2016 253 x 177 mm 720pp 978-1-107-04316-9 Hardback £59.99 / US$99.99 For all formats available, see

www.cambridge.org/9781107043169

Confidence, Likelihood, Probability Statistical Inference with Confidence Distributions Tore Schweder Universitetet i Oslo

and Nils Lid Hjort Universitetet i Oslo

The baby without the bathwater? This lively book lays out a methodology of confidence distributions and puts them through their paces with a generous mixture of theory, illustrations, applications and exercises – suitable for statisticians at all levels of experience, as well as for data-oriented scientists. The methodology yields posterior distributions for parameters, but without the Bayesian ingredients. ‘This book presents a detailed and wide-ranging account of an approach to inference that moves the discipline towards increased cohesion, avoiding the artificial distinction between testing and estimation. Innovative and thorough, it is sure to have an impact both in the foundations of inference and in a wide range of practical applications of inference.’ Nancy Reid, University Professor of Statistical Sciences, University of Toronto Cambridge Series in Statistical and Probabilistic Mathematics, 41

2016 253 x 177 mm 511pp 147 b/w illus. 17 tables 100 exercises 978-0-521-86160-1 Hardback £52.99 / US$84.99 For all formats available, see

www.cambridge.org/9780521861601

Probability Textbook

Measures, Integrals and Martingales Second edition René L. Schilling Technische Universität, Dresden

Measure and integration are key topics in many areas of mathematics, including analysis, probability, mathematical physics and finance. This book offers a concise yet elementary introduction in which the theory is quickly and simply developed. Few prerequisites are required, making the text suitable for undergraduate lecture courses or self-study. Contents: List of symbols; Prelude; Dependence chart; 1. Prologue; 2. The pleasures of counting; 3. σ-algebras; 4. Measures; 5. Uniqueness of measures; 6. Existence of measures; 7. Measurable mappings; 8. Measurable functions; 9. Integration of positive functions; 10. Integrals of measurable functions; 11. Null sets and the ‘almost everywhere’; 12. Convergence theorems and their applications; 13. The function spaces Lp; 14. Product measures and Fubini’s theorem; 15. Integrals with respect to image measures; 16. Jacobi’s transformation theorem; 17. Dense and determining sets; 18. Hausdorff measure; 19. The Fourier transform; 20. The Radon–Nikodym theorem; 21. Riesz representation theorems; 22. Uniform integrability and Vitali’s convergence theorem; 23. Martingales; 24. Martingale convergence theorems; 25. Martingales in action; 26. Abstract Hilbert spaces; 27. Conditional expectations; 28. Orthonormal systems and their convergence behaviour; Appendix A. Lim inf and lim sup; Appendix B. Some facts from topology; Appendix C. The volume of a parallelepiped; Appendix D. The integral of complex valued functions; Appendix E. Measurability of the continuity points of a function; Appendix F. Vitali’s covering theorem; Appendix G. Non-measurable sets; Appendix H. Regularity of measures; Appendix I. A summary of the Riemann integral; References; Name and subject index. Review of previous edition: ‘… [a] thorough introduction to a wide variety of first-year graduate-level topics in analysis … accessible to anyone with a strong undergraduate background in calculus, linear algebra and real analysis.’ Zentralblatt MATH 2017 247 x 174 mm 512pp 40 b/w illus. 420 exercises 978-1-316-62024-3 Paperback £39.99 / US$54.99 For all formats available, see

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Probability

Long-Range Dependence and Self-Similarity Vladas Pipiras University of North Carolina, Chapel Hill

and Murad S. Taqqu Boston University

Real-world time series rarely satisfy simple assumptions, often exhibiting long-range dependence. Ignoring this undermines accurate detection of trends and other important behavior. This text for graduate students and researchers in statistics and probability is also a reference for specialists in fields such as economics, finance, and hydrology. ‘This is a marvelous book that brings together both classical background material and the latest research results on long-range dependence. The book is written so that it can be used as a main source by a graduate student, including all the essential proofs. I highly recommend this book.’ Mark M. Meerschaert, Michigan State University Cambridge Series in Statistical and Probabilistic Mathematics, 45

2017 253 x 177 mm 382pp 58 b/w illus. 8 tables 978-1-107-03946-9 Hardback £71.99 / US$89.99 For all formats available, see

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Random Walks and Heat Kernels on Graphs Martin T. Barlow University of British Columbia, Vancouver

This introduction to random walks on infinite graphs, in both discrete and continuous time, gives a systematic account of transition densities, including useful but hard-to-find results. The book is aimed at researchers and graduate students in mathematics who have a basic familiarity with analysis and some familiarity with probability. London Mathematical Society Lecture Note Series, 438

2017 228 x 152 mm 236pp 5 b/w illus. 8 exercises 978-1-107-67442-4 Paperback £50.00 / US$80.00 For all formats available, see

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Fractals in Probability and Analysis

Non-homogeneous Random Walks

Christopher J. Bishop

Lyapunov Function Methods for Near-Critical Stochastic Systems Mikhail Menshikov

Stony Brook University, State University of New York

and Yuval Peres Microsoft Research, Washington

This book offers a mathematically rigorous introduction to fractals, emphasizing examples and fundamental ideas while minimizing technicalities. The clear presentation of a broad range of techniques makes the volume accessible to graduate students, while the independent nature of chapters renders it a useful supplement or resource for a variety of courses. ‘Fractal sets are now a key ingredient of much of mathematics, ranging from dynamical systems, transformation groups, stochastic processes, to modern analysis. This delightful book gives a correspondingly broad view of fractal sets. The presentation is original, clear and thoughtful, often with new and interesting approaches. It is suited both to graduate students and researchers, discussing reasonably easily accessible questions as well as research topics that are being actively investigated today. For example, in addition to learning about fractals, students will get new insights into some core topics, such as Brownian motion, while researchers will find new ideas for up-to-date research, for example, related to analysts’ traveling salesman problems. The book is splendid for a variety of graduate courses, with most sections being essentially independent of each other, and is supported by a very large number of exercises of varying levels with hints and solutions.’ Pertti Mattila, University of Helsinki Cambridge Studies in Advanced Mathematics, 162

2016 228 x 152 mm 412pp 75 b/w illus. 380 exercises 978-1-107-13411-9 Hardback £49.99 / US$79.99 For all formats available, see

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University of Durham

Serguei Popov Universidade Estadual de Campinas, Brazil

and Andrew Wade University of Durham

A modern presentation of the ‘Lyapunov function’ method applied to near-critical stochastic systems, exemplified by nonhomogeneous random walks. Aimed at researchers and research students in probability theory or a neighbouring field, the material will be accessible to anyone with some familiarity with the theory of Markov chains and discretetime martingales. Cambridge Tracts in Mathematics, 209

2016 228 x 152 mm 382pp 20 b/w illus. 978-1-107-02669-8 Hardback £110.00 / US$140.00 For all formats available, see

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Stochastic Analysis Itô and Malliavin Calculus in Tandem Hiroyuki Matsumoto Aoyama Gakuin University, Japan

and Setsuo Taniguchi Kyushu University, Japan

This compact, graduate-level text develops the Itô calculus and the Malliavin calculus in tandem, laying out a balanced toolbox for researchers and students in mathematics and mathematical finance, and taking readers from foundations to current, groundbreaking applications. ‘This book is a comprehensive guide to stochastic analysis related to Brownian motion. It contains the basis of the Itô calculus and the Malliavin calculus, which are the heart of the modern analysis of Brownian motion. The book is self-contained and it is accessible for graduate students and researchers who wish to learn about stochastic differential equations.’ Hiroshi Kunita, author of Stochastic Flows and Stochastic Differential Equations (Cambridge, 1990) Cambridge Studies in Advanced Mathematics, 159

2016 228 x 152 mm 357pp 978-1-107-14051-6 Hardback £44.99 / US$69.99 For all formats available, see

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Probability

Gaussian Processes on Trees From Spin Glasses to Branching Brownian Motion Anton Bovier

biology as well as other branches of mathematics.

audience, this book is a much-needed introduction to the basic concepts.

Contents: Part I. Regular Convergence; Part II. Irregular Convergence; Part III. Convergence of Cosine Families; Part IV. Appendices.

Contents: Part I. Symmetric Functions and Thoma’s Theorem; Part II. Unitary Representations.

Rheinische Friedrich-Wilhelms-Universität Bonn

New Mathematical Monographs, 30

Cambridge Studies in Advanced Mathematics, 160

Branching Brownian motion is a key model at the crossroads of value statistics for Gaussian processes, statistical physics, and non-linear partial differential equations. This book gives a concise introduction for graduate students and researchers leading up to the most recent developments in this active area of research.

2016 228 x 152 mm 454pp 60 b/w illus. 9 colour illus. 160 exercises 978-1-107-13743-1 Hardback £89.99 / US$140.00

2016 228 x 152 mm 168pp 2 b/w illus. 80 exercises 978-1-107-17555-6 Hardback £44.99 / US$64.99

For all formats available, see

Cambridge Studies in Advanced Mathematics, 163

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An Introduction to the Theory of Reproducing Kernel Hilbert Spaces

Groups, Graphs and Random Walks

2016 228 x 152 mm 210pp 4 b/w illus. 978-1-107-16049-1 Hardback £44.99 / US$69.99

Vern I. Paulsen

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United Services Automobile Association

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Probability on Real Lie Algebras Uwe Franz Université de Franche-Comté

and Nicolas Privault Nanyang Technological University, Singapore

University of Waterloo, Ontario

and Mrinal Raghupathi

Covering the fundamental underlying theory as well as a range of applications, this unique text provides a unified overview of reproducing kernel Hilbert spaces. It offers an unrivalled and accessible introduction to the field, ideal for graduate students and researchers working in functional analysis or its applications.

This monograph is a progressive introduction to non-commutativity in probability theory, summarizing and synthesizing recent results about classical and quantum stochastic processes on Lie algebras. This book will appeal to advanced undergraduate and graduate students interested in the relations between algebra, probability, and quantum theory.

Contents: Part I. General Theory; Part II. Applications and Examples.

Cambridge Tracts in Mathematics, 206

Representations of the Infinite Symmetric Group

2016 228 x 152 mm 302pp 2 b/w illus. 27 exercises 978-1-107-12865-1 Hardback £79.99 / US$125.00 For all formats available, see

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Convergence of One-Parameter Operator Semigroups In Models of Mathematical Biology and Elsewhere Adam Bobrowski Politechnika Lubelska, Poland

Written by a leading expert in the field, this book presents the classical theory of convergence of semigroups and then uses real examples to show how it can be applied to models of mathematical

Cambridge Studies in Advanced Mathematics, 152

Edited by Tullio Ceccherini-Silberstein Università degli Studi del Sannio, Italy

Maura Salvatori Università degli Studi di Milano

and Ecaterina Sava-Huss Cornell University, New York

An accessible and panoramic account of the theory of random walks on groups and graphs, stressing the connections with other branches of mathematics. Covering topics from growth and amenability of groups, Schrödinger operators, and Poisson boundaries, this book provides a valuable and up-todate reference for both researchers and graduates. London Mathematical Society Lecture Note Series, 436

For all formats available, see

2017 228 x 152 mm 542pp 70 b/w illus. 20 exercises 978-1-316-60440-3 Paperback £65.00 / US$115.00 Publication June 2017

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2016 228 x 152 mm 192pp 99 exercises 978-1-107-10409-9 Hardback £44.99 / US$69.99

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Alexei Borodin

Random Graphs, Geometry and Asymptotic Structure

Massachusetts Institute of Technology

Michael Krivelevich

and Grigori Olshanski

Tel-Aviv University

Institute for Information Transmission Problems, Russian Academy of Sciences

Konstantinos Panagiotou

Representation theory of big groups is an important and quickly developing part of modern mathematics, giving rise to a variety of important applications in probability and mathematical physics. Offering a concise and self-contained exposition accessible to a wide

Mathew Penrose

Universität Munchen University of Bath

and Colin McDiarmid University of Oxford

Edited by Nikolaos Fountoulakis University of Birmingham

and Dan Hefetz University of Birmingham

A self-contained and concise introduction to recent developments, particularly those of a geometric and topological nature, in the theory of random graphs. Such material is seldom covered in the formative study of young combinatorialists and probabilists,

Probability / Actuarial Science making this essential reading for beginning researchers in these fields. Contents: Part I. Long Paths and Hamiltonicity in Random Graphs; Part II. Random Graphs from Restricted Classes; Part III. Lectures on Random Geometric Graphs; Part IV. On Random Graphs from a Minorclosed Class. London Mathematical Society Student Texts, 84

2016 228 x 152 mm 127pp 2 b/w illus. 1 table 978-1-107-13657-1 Hardback £49.99 / US$84.99 978-1-316-50191-7 Paperback £24.99 / US$39.99 For all formats available, see

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Actuarial Science Financial Enterprise Risk Management Second edition Paul Sweeting Legal and General Investment Management, London

An accessible guide to enterprise risk management for financial institutions, containing all the tools needed to build and maintain an ERM framework. This new expanded edition has been thoroughly updated to reflect new legislation and the creation of the Financial Conduct Authority and the Prudential Regulation Authority. Review of previous edition: ‘Provides all the tools required to build and maintain a comprehensive ERM framework, covering a range of qualitative and quantitative techniques and their uses in identifying, assessing, modelling and measuring risk.’ Actuary Magazine International Series on Actuarial Science

2017 247 x 174 mm 600pp 120 b/w illus. 25 tables 150 exercises 978-1-107-18461-9 Hardback c. £84.99 / c. US$130.00 Publication June 2017 For all formats available, see

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Insurance Risk and Ruin Second edition David C. M. Dickson University of Melbourne

Designed as a first course in insurance risk theory for readers with a solid understanding of basic probability, this book develops the two major areas of risk theory: aggregate claims distributions and ruin theory. The new edition includes additional exercises and expanded sections on contemporary topics. International Series on Actuarial Science

2016 228 x 152 mm 304pp 30 b/w illus. 18 tables 978-1-107-15460-5 Hardback £39.99 / US$64.99 For all formats available, see

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and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data. Contents: Part I. Predictive Modeling Foundations; Part II. Predictive Modeling Methods; Part III. Bayesian and Mixed Modeling; Part IV. Longitudinal Modeling. ‘With contributions coming from a wide variety of researchers, professors, and actuaries – including several CAS Fellows – it’s clear that this book will be valuable for any P and C actuary whose main concern is using predictive modeling in his or her own work.’ David Zornek, Actuarial Review International Series on Actuarial Science

2014 247 x 174 mm 563pp 120 b/w illus. 94 tables 26 exercises 978-1-107-02987-3 Hardback £52.00 / US$88.00 For all formats available, see

Leases for Lives Life Contingent Contracts and the Emergence of Actuarial Science in Eighteenth-Century England David R. Bellhouse University of Western Ontario

This important work illuminates the early modern roots of two very contemporary concerns, real estate values and pension/ annuity funding. It places in historical context the work of an active group of eighteenth-century mathematicians, and gives a resonant example of the vagaries of ‘technology transfer’ from mathematical sciences to commercial activities. 2017 228 x 152 mm 284pp 978-1-107-11176-9 Hardback £95.00 / US$125.00 Publication July 2017 For all formats available, see

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Predictive Modeling Applications in Actuarial Science Volume 1: Predictive Modeling Techniques Edited by Edward W. Frees University of Wisconsin, Madison

Richard A. Derrig

www.cambridge.org/9781107029873

Predictive Modeling Applications in Actuarial Science Volume 2: Case Studies in Insurance Edited by Edward W. Frees University of Wisconsin, Madison

Glenn Meyers ISO Innovative Analytics, New Jersey

and Richard A. Derrig Temple University, Philadelphia

Predictive modeling involves the use of data to forecast future events. Building on the foundations developed in the first volume, Volume 2 examines applications of predictive modeling, focusing on property and casualty insurance, exposing readers to a variety of techniques in real-life contexts that demonstrate the value of predictive modeling. Praise for Volume 1: ‘With contributions coming from a wide variety of researchers, professors, and actuaries – including several CAS Fellows – it’s clear that this book will be valuable for any actuary whose main concern is using predictive modeling in his or her own work.’ David Zornek, Actuarial Review

Temple University, Philadelphia

International Series on Actuarial Science

and Glenn Meyers

2016 247 x 174 mm 330pp 978-1-107-02988-0 Hardback £72.00 / US$89.99

ISO Innovative Analytics, New Jersey

This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. It emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications,

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Actuarial Science Textbook

Actuarial Mathematics for Life Contingent Risks

Nonlife Actuarial Models

Second edition David C. M. Dickson

Theory, Methods and Evaluation Yiu-Kuen Tse

University of Melbourne

Singapore Management University

Mary R. Hardy

Actuaries must pass exams, but more than that: they must put knowledge into practice. This coherent textbook gives complete syllabus coverage for Exam C of the Society of Actuaries (SOA) while emphasizing the concepts and practical application of nonlife actuarial models. It has also been class-tested for undergraduate university courses.

University of Waterloo, Ontario

and Howard R. Waters Heriot-Watt University, Edinburgh

Three leaders in actuarial science give a modern perspective on life contingencies. Balancing rigour and intuition, and emphasizing applications, it is ideal for university courses and actuarial exam preparation. This second edition includes new chapters and exercises to help prepare for the MLC Models for Life Contingencies exam. Review of the first edition: ‘… well written, well organized, and easy to read. … an excellent textbook for both undergraduate and graduate programs in actuarial science. It is also a rich source of useful information for practitioners of the actuarial profession and financial risk managers who seek a practical and inspiring guide to liability cash flow modeling and valuation.’ Scandinavian Actuarial Journal International Series on Actuarial Science

2013 228 x 152 mm 616pp 50 b/w illus. 90 tables 210 exercises 978-1-107-04407-4 Hardback £74.99 / US$99.99 For all formats available, see

Contents: Preface; Notation and convention; Part I. Loss Models: 1. Claim-frequency distribution; 2. Claim-severity distribution; 3. Aggregate-loss models; Part II. Risk and Ruin: 4. Risk measures; 5. Ruin theory; Part III. Credibility: 6. Classical credibility; 7. Bühlmann credibility; 8. Bayesian approach; 9. Empirical implementation of credibility; Part IV. Model Construction and Evaluation: 10. Model estimation and types of data; 11. Nonparametric model estimation; 12. Parametric model estimation; 13. Model evaluation and selection; 14. Basic Monte Carlo methods; 15. Applications of Monte Carlo methods; Appendix. Review of statistics; Answers to exercises; References; Index. ‘… a very good balance of rigor and readability makes this book an impressive encyclopedia of results and methods for nonlife actuarial modeling.’ Zentralblatt MATH

www.cambridge.org/9781107044074

International Series on Actuarial Science

Solutions Manual for Actuarial Mathematics for Life Contingent Risks

For all formats available, see

Second edition David C. M. Dickson University of Melbourne

Mary R. Hardy University of Waterloo, Ontario

and Howard R. Waters Heriot-Watt University, Edinburgh

A must-have manual providing solutions to all exercises in the authors’ groundbreaking text, which is required reading for the Society of Actuaries (SOA) Exam MLC, covering virtually the whole syllabus for the UK Subject CT5 exam. Over two hundred solutions provide insight and exam preparation. Companion spreadsheets are freely available online. International Series on Actuarial Science

2013 228 x 152 mm 227pp 978-1-107-62026-1 Paperback £32.99 / US$40.99 For all formats available, see

www.cambridge.org/9781107620261

2009 228 x 152 mm 542pp 1 b/w illus. 55 tables 350 exercises 978-0-521-76465-0 Hardback £49.99 / US$89.99 www.cambridge.org/9780521764650

Generalized Linear Models for Insurance Data Piet de Jong Macquarie University, Sydney

and Gillian Z. Heller Macquarie University, Sydney

Actuaries should have the tools they need. Practical and rigorous, this book introduces generalized linear models in the actuarial context. All techniques are illustrated on data sets relevant to insurance. Exercises and data-based practicals allow skill consolidation. SAS

code and output, data sets, and exercise solutions are available on the website. International Series on Actuarial Science

2008 228 x 152 mm 208pp 34 b/w illus. 5 colour illus. 43 tables 25 exercises 978-0-521-87914-9 Hardback £70.00 / US$98.00 For all formats available, see

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Regression Modeling with Actuarial and Financial Applications Edward W. Frees University of Wisconsin, Madison

This book gives actuarial and finance students a foundation in multiple regression and time series, discussing advanced statistical topics that are relevant to actuarial and financial practice. It uses statistical techniques to analyze real data in risk management and finance. No specific knowledge of these areas is presumed. Contents: Part I. Linear Regression; Part II. Topics in Time Series; Part III. Topics in Nonlinear Regression; Part IV. Actuarial Applications. ‘It would be an ideal text for a semester – or a year-long course in applied statistical methods for actuarial science majors. … it would also be a welcome addition to the bookshelf of practicing actuaries at all levels, both actuarial students charged with conducting analyses for which the methods discussed in the book are most relevant, and senior managers who use such analyses as a basis for financial decision making … Perhaps my favorite part of Frees’ book is the final two chapters. If these fine essays do not already appear somewhere on the Society of Actuaries syllabus, they should be added immediately.’ Ronald C. Neath, The American Statistician International Series on Actuarial Science

2010 247 x 174 mm 584pp 139 b/w illus. 142 tables 89 exercises 978-0-521-76011-9 Hardback £108.00 / US$196.00 978-0-521-13596-2 Paperback £45.99 / US$77.00 For all formats available, see

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Finance and Economics

is for all those interested in public policy arguments about insurance and discrimination: policymakers, academics, actuaries, underwriters, disability activists, geneticists and other medical professionals.

Handbook of Spectrum Auction Design

Contents: Part I. Introduction; Part II. Loss Coverage; Part III. Further Aspects of Risk Classification; Part IV. Conclusion.

Edited by Martin Bichler Technische Universität München

and Jacob K. Goeree University of New South Wales, Sydney

Governments worldwide use spectrum auctions to assign and price licenses for wireless communications, a key resource for any mobile operator. This comprehensive Handbook by an international team of experts covers the pros and cons of different auction formats and lessons learned from theory, experiments, and the field.

Advance praise: ‘Guy Thomas challenges the orthodox views held by the insurance industry, actuaries, and economists concerning the problem of adverse selection. He makes his case that a little adverse selection is actually a good thing in a sensible, pragmatic, and compelling manner. His critical insights about the debates on restricting risk classification in insurance should be essential reading for policy makers.’ Michael Hoy, University of Guelph, Canada

Contents: Part I. The Simultaneous Ascending Auction; Part II. The Combinatorial Clock Auction Designs; Part III. Alternative Auction Designs; Part IV. Experimental Comparisons of Auction Designs; Part V. The Bidders’ Perspective; Part VI. Secondary Markets and Exchanges.

2017 228 x 152 mm 200pp 25 b/w illus. 9 tables 978-1-107-10033-6 Hardback £67.99 / US$84.99 978-1-107-49590-6 Paperback £21.99 / US$27.99 Publication May 2017

2017 253 x 177 mm 904pp 978-1-107-13534-5 Hardback c. £79.99 / c. US$99.99 Publication July 2017

www.cambridge.org/9781107100336

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The Elements of Financial Econometrics Jianqing Fan Princeton University, New Jersey

and Qiwei Yao London School of Economics and Political Science

A compact, master’s-level textbook on financial econometrics, focusing on methodology and including real financial data illustrations throughout. The mathematical level is purposely kept moderate, allowing the power of the quantitative methods to be understood without too much technical detail. 2017 247 x 174 mm 392pp 6 b/w illus. 92 colour illus. 26 tables 90 exercises 978-1-107-19117-4 Hardback £54.99 / US$69.99 For all formats available, see

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Loss Coverage Why Insurance Works Better with Some Adverse Selection Guy Thomas

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Textbook

Credit Risk Marek Capiński AGH University of Science and Technology, Krakow

and Tomasz Zastawniak University of York

This comprehensive and accessible introduction to modelling credit risk is tailored for master’s students. It focuses on the two mainstream approaches, structural models and reduced form models, and on pricing selected credit risk derivatives. Balancing rigorous theory with financial intuition, it features detailed worked examples and exercises. Contents: Preface; 1. Structural models; 2. Hazard function model and no arbitrage; 3. Defaultable bond pricing with hazard function; 4. Security pricing with hazard function; 5. Hazard process model; 6. Security pricing with hazard process; Appendix; Selected literature; Index. Mastering Mathematical Finance

Textbook

Twenty Lectures on Algorithmic Game Theory Tim Roughgarden Stanford University, California

This book gives students a quick and accessible introduction to many of the most important concepts in the field of algorithmic game theory. It demonstrates these concepts through case studies in online advertising, wireless spectrum auctions, kidney exchange, and network management. Contents: 1. Introduction and examples; 2. Mechanism design basics; 3. Myerson’s Lemma; 4. Algorithmic mechanism design 34; 5. Revenue-maximizing auctions; 6. Simple near-optimal auctions; 7. Multiparameter mechanism design; 8. Spectrum auctions; 9. Mechanism design with payment constraints; 10. Kidney exchange and stable matching; 11. Selfish routing and the price of anarchy; 12. Network over-provisioning and atomic selfish routing; 13. Equilibria: definitions, examples, and existence; 14. Robust price-of-anarchy bounds in smooth games; 15. Best-case and strong Nash equilibria; 16. Best-response dynamics; 17. No-regret dynamics; 18. Swap regret and the Minimax theorem; 19. Pure Nash equilibria and PLS-completeness; 20. Mixed Nash equilibria and PPAD-completeness. 2016 228 x 152 mm 250pp 978-1-107-17266-1 Hardback £59.99 / US$99.99 978-1-316-62479-1 Paperback £27.99 / US$34.99 For all formats available, see

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Applied Conic Finance Dilip Madan University of Maryland, College Park

and Wim Schoutens Katholieke Universiteit Leuven, Belgium

This book introduces the new theory of conic finance, or two-price theory, which determines bid and ask prices in a consistent and motivated manner. The authors cover the fundamentals of the theory, various advanced quantitative models and numerous real-world applications, with practical examples and case studies.

2016 228 x 152 mm 202pp 6 b/w illus. 978-1-107-00276-0 Hardback £49.99 / US$79.99 978-0-521-17575-3 Paperback £27.99 / US$39.99

2016 247 x 174 mm 198pp 95 b/w illus. 20 tables 978-1-107-15169-7 Hardback £64.99 / US$99.99

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University of Kent, Canterbury

A novel book that argues that, contrary to received wisdom, some adverse selection in insurance markets is beneficial to society as a whole. It

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Finance and Economics / Life sciences, Medicine and Health

Financial Analytics with R Building a Laptop Laboratory for Data Science Mark J. Bennett University of Chicago

and Dirk L. Hugen University of Iowa

This book provides the intuition and basic vocabulary as steps towards the financial, statistical, and algorithmic knowledge needed to resolve current industry problems, while also presenting a systematic way of developing analytical programs for finance in the statistical language R. This book is a key training resource for students and professionals alike. ‘A very well-written text on financial analytics, focusing on developing statistical models and using simulation to better understand financial data. R is used throughout for examples, allowing the reader to use the text and code to actively engage in the financial market. It is simply the best text on this subject that I have seen. Highly recommended.’ Joseph M. Hilbe, Arizona State University 2016 247 x 174 mm 392pp 60 b/w illus. 100 colour illus. 40 exercises 978-1-107-15075-1 Hardback £44.99 / US$74.99 For all formats available, see

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Limit Order Books Frédéric Abergel CentraleSupélec, France

Marouane Anane BNP Paribas, France

Anirban Chakraborti Jawaharlal Nehru University, India

Aymen Jedidi HSBC Bank, France

and Ioane Muni Toke Université de la Nouvelle-Calédonie

Limit order books and order-driven markets form one of the main fields in market microstructure, an area which has triggered a considerable amount of interest amongst both researchers and market practitioners. This text is devoted to the statistical, mathematical and numerical aspects of limit order books. Physics of Society: Econophysics and Sociophysics

2016 244 x 170 mm 238pp 978-1-107-16398-0 Hardback £44.99 / US$64.99 For all formats available, see

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Contest Theory Incentive Mechanisms and Ranking Methods Milan Vojnović London School of Economics and Political Science

Contests are prevalent in many areas, from sports, to labor markets, to resource allocation, to crowdsourcing. Using a game-theoretic framework, this unified, comprehensive treatment pays particular attention to online applications of contest design, allowing professionals, researchers and students to learn about the theoretical principles and to test them in practice. ‘Contest theory, including the war of attrition, winner-take-all competition, and tournaments, has recently received renewed attention, due to its applicability to online rating systems, platform competition, and other internet phenomena. Milan Vojnović’s book is a delightful and thorough examination of the state of the art in contest modeling, for economists and computer scientists alike.’ Preston McAfee, chief economist, Microsoft 2016 253 x 177 mm 730pp 187 b/w illus. 6 tables 978-1-107-03313-9 Hardback £49.99 / US$79.99 For all formats available, see

www.cambridge.org/9781107033139

Life sciences, Medicine and Health Planning Clinical Research Robert A. Parker Harvard Medical School, Massachusetts

and Nancy Greene Berman Harbor-UCLA Medical Center, Los Angeles

The authors draw on their many years working in clinical research to offer investigators practical guidance on the essential elements of study planning. Short chapters focus on specific clinical design aspects and a host of real-world examples illustrate key features and demonstrate what works and what does not. Contents: Part I. Introduction; Part II. Study Designs; Part III. Core Concepts Applicable to All Study Designs; Part IV. Additional Concepts for Interventional Studies; Part V. Additional Concepts for Observational Studies; Part VI. Practical Issues. 2016 247 x 174 mm 352pp 978-0-521-84063-7 Hardback £59.99 / US$99.99 978-0-521-54995-0 Paperback £29.99 / US$44.99 For all formats available, see

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Textbook

Experimental Design for Laboratory Biologists Maximising Information and Improving Reproducibility Stanley E. Lazic AstraZeneca

An ideal resource for anyone conducting lab-based biomedical research, this guide shows how to design reproducible experiments that have low bias, high precision and widely applicable results. It explores key ideas in experimental design, including reproducibility and replication, assesses common designs, and shows how to plan for success. Contents: 1. Introduction: 1.1 What is reproducibility?; 1.2 The psychology of scientific discovery; 1.3 Are most published results wrong?; 1.4 Frequentist statistical interference; 1.5 Which statistics software to use?; 2. Key ideas in experimental design: 2.1 Learning versus confirming experiments; 2.2 The fundamental experimental design equation; 2.3 Randomisation; 2.4 Blocking; 2.5 Blinding; 2.6 Effect type: fixed versus random; 2.7 Factor arrangement: crossed

Life sciences, Medicine and Health versus nested; 2.8 Interactions between variables; 2.9 Sampling; 2.10 Use of controls; 2.11 Front-aligned versus endaligned designs; 2.12 Heterogeneity and confounding; 3. Replication (what is ‘N’?): 3.1 Biological units; 3.2 Experimental units; 3.3 Observational units; 3.4 Relationship between units; 3.5 How is the experimental unit defined in other disciplines?; 4. Analysis of common designs: 4.1 Preliminary concepts; 4.2 Background to the designs; 4.3 Completely randomised designs; 4.4 Randomised block designs; 4.5 Split-unit designs; 4.6 Repeated measures designs; 5. Planning for success: 5.1 Choosing a good outcome variable; 5.2 Power analysis and sample size calculations; 5.3 Optimal experimental designs (rules of thumb); 5.4 When to stop collecting data?; 5.5 Putting it all together; 5.6 How to get lucky; 5.7 The statistical analysis plan; 6. Exploratory data analysis: 6.1 Quality control checks; 6.2 Preprocessing; 6.3 Understanding the structure of the data; Appendix A. Introduction to R; Appendix B. Glossary. ‘This is a wonderfully lucid introduction to experimental design, written by an author who is clearly aware of the pitfalls that exist for the unwary experimenter. The focus is on how to design experiments to ensure reproducible research, with many examples illustrating general principles that need to be understood to avoid error and bias. The coverage of statistical analysis follows on naturally from the design issues, and is amply illustrated with exercises in R. Highly recommended.’ Dorothy Bishop, University of Oxford 2016 246 x 189 mm 229pp 124 b/w illus. 978-1-107-07429-3 Hardback £99.99 / US$175.00 978-1-107-42488-3 Paperback £39.99 / US$64.99 For all formats available, see

www.cambridge.org/9781107074293

Cause and Correlation in Biology A User’s Guide to Path Analysis, Structural Equations and Causal Inference with R Second edition Bill Shipley

text in a SEM course given within any discipline, and can be used by scholars and researchers from any area of science.’ Structural Equation Modeling

2016 228 x 152 mm 476pp 147 b/w illus. 23 colour illus. 31 tables 978-1-107-06911-4 Hardback £82.00 / US$129.00

For all formats available, see

www.cambridge.org/9781107442597

www.cambridge.org/9781107069114

Bioinformatics and Computational Biology in Drug Discovery and Development

Genome-Wide Association Studies

Edited by William T. Loging

GeneExpression Systems, Inc., Massachusetts

Review of previous edition: ‘… the perfect introduction to SEM. This book can be used as the primary

From Polymorphism to Personalized Medicine Edited by Krishnarao Appasani

Icahn School of Medicine, Mount Sinai, New York

Foreword by Stephen W. Scherer

Providing a comprehensive overview of the drug discovery and development pipeline, this book focuses on the integral role that computational biology methods play in this process. Covering technological advances and their relation to drug developmental processes, readers are exposed to new methods of discovery utilising the available technology.

University of Toronto

John Nosta, President, NOSTALAB 2016 247 x 174 mm 244pp 46 b/w illus. 9 colour illus. 3 tables 978-0-521-76800-9 Hardback £44.99 / US$74.99 For all formats available, see

www.cambridge.org/9780521768009

Université de Sherbrooke, Canada

Written for biologists and students, this practical guide underlies the principle methods for analysing cause-effect relationships. Featuring extensive sections on the use of R statistical language to apply statistical methods to biological data, this completely revised new edition is a valuable resource for practising biologists.

in the field to handle information integration problems of omics data. Popular technologies include microarray, next-generation sequencing, mass spectrometry and proteomic assays.

2016 247 x 174 mm 314pp 113 b/w illus. 22 tables 978-1-107-44259-7 Paperback £39.99 / US$64.99

‘We live in transformative times and perhaps big data and analytics represent a most interesting and essential concept of today. From the telescope to the microscope, our ability to peer into complexities of life has transformed humanity. Big data is the next major step in major transition. Bioinformatics and Computational Biology in Drug Discovery and Development is a road map to an inevitable future – a future where data define disease, diagnosis and drugs. This book is an essential companion for anyone in drug development who has one foot in the present and one in the future.’

Integrating Omics Data George Tseng University of Pittsburgh

Debashis Ghosh Colorado School of Public Health

and Xianghong Jasmine Zhou University of Southern California

This book provides comprehensive coverage of information integration of omics, experimental data, and databases. It introduces state-of-theart methods developed by leaders

and Peter M. Visscher University of Queensland

Genome-wide association studies (GWAS) will have increasing importance in the post-genomic era. This timely publication, written by leading experts from academia and industry, provides an essential overview for both established scientists and students, focusing on the use of GWAS in the context of disease biology and personalized medicine. Contents: Part I. Genome-Wide Association Studies; Part II. Genome-Wide Studies in Disease Biology; Part III. Single Nucleotide Polymorphisms, Copy Number Variants, Haplotypes and eQTLs; Part IV. NextGeneration Sequencing Technology and Pharmacogenomics; Part V. Population Genetics and Personalized Medicine. ‘Genome-Wide Association Studies: From Polymorphism to Personalized Medicine, edited by Krishnarao Appasani, summarizes most elegantly the contributions of GWAS as a major discovery tool linking complex disease phenotypes to genetic variants and associated biological pathways and gene networks that were previously unknown. GWAS has transformed the genetic landscape in complex disease and has informed us more about the genetic underpinnings of common diseases and pharmacogenomics traits than any other tool to date. The present book captures this development elegantly and is a pleasure to read.’ Hakon Hakonarson, University of Pennsylvania 2016 247 x 174 mm 432pp 67 b/w illus. 26 colour illus. 24 tables 978-1-107-04276-6 Hardback £115.00 / US$185.00 For all formats available, see

www.cambridge.org/9781107042766

eBooks available at www.cambridge.org/ebookstore

Computer Science

Computer Science Textbook

Probability and Computing Randomization and Probabilistic Techniques in Algorithms and Data Analysis Second edition Michael Mitzenmacher Harvard University, Massachusetts

and Eli Upfal Brown University, Rhode Island

This greatly expanded new edition, requiring only an elementary background in discrete mathematics, comprehensively covers randomization and probabilistic techniques in modern computer science. It includes new material relevant to machine learning and big data analysis, plus examples and exercises, enabling students to learn modern techniques and applications. Contents: 1. Events and probability; 2. Discrete random variables and expectations; 3. Moments and deviations; 4. Chernoff and Hoeffding bounds; 5. Balls, bins, and random graphs; 6. The probabilistic method; 7. Markov chains and random walks; 8. Continuous distributions and the Polsson process; 9. The normal distribution; 10. Entropy, randomness, and information; 11. The Monte Carlo method; 12. Coupling of Markov chains; 13. Martingales; 14. Sample complexity, VC dimension, and Rademacher complexity; 15. Pairwise independence and universal hash functions; 16. Power laws and related distributions; 17. Balanced allocations and cuckoo hashing. Advance praise: ‘As randomized methods continue to grow in importance, this textbook provides a rigorous yet accessible introduction to fundamental concepts that need to be widely known. The new chapters in this second edition, about sample size and power laws, make it especially valuable for today’s applications.’ Donald E. Knuth, Stanford University, California 2017 253 x 177 mm 488pp 8 b/w illus. 1 table 978-1-107-15488-9 Hardback £44.99 / US$69.99 Publication May 2017 For all formats available, see

www.cambridge.org/9781107154889

Algorithms and Models for Network Data and Link Analysis François Fouss Université Catholique de Louvain, Belgium

Marco Saerens Université Catholique de Louvain, Belgium

and Masashi Shimbo Nara Institute of Science and Technology, Japan

Network data capture social and economic behavior in a form that can be analyzed using computational tools. In this entry-level guide, algorithms for extracting information are derived in detail and summarized in pseudocode. This book is intended primarily for computer scientists, engineers, statisticians and physicists, but it is also accessible to social network scientists more broadly. ‘This is a remarkable book that contains a coherent and unified presentation of many recent network data analysis concepts and algorithms. Rich with details and references, this is a book from which faculty and students alike will learn a lot!’ Vincent Blondel, Université Catholique de Louvain, Belgium 2016 253 x 177 mm 543pp 14 b/w illus. 7 tables 978-1-107-12577-3 Hardback £64.00 / US$79.99 For all formats available, see

www.cambridge.org/9781107125773

Statistical Methods for Recommender Systems Deepak K. Agarwal LinkedIn Corporation, California

and Bee-Chung Chen LinkedIn Corporation, California

This book is for researchers and students in statistics, data mining, computer science, machine learning and marketing, and also practitioners who implement recommender systems. It provides an in-depth discussion of challenges encountered in deploying real-life large-scale systems and stateof-the-art solutions in personalization, explore/exploit, dimension reduction and multi-objective optimization. Contents: Part I. Introduction; Part II. Common Problem Settings; Part III. Advanced Topics. 2016 228 x 152 mm 298pp 66 b/w illus. 18 tables 978-1-107-03607-9 Hardback £34.99 / US$59.99 For all formats available, see

www.cambridge.org/9781107036079

Sparse Image and Signal Processing Wavelets and Related Geometric Multiscale Analysis Second edition Jean-Luc Starck Centre d’etudes de Saclay, France

Fionn Murtagh Goldsmiths University of London and University of Derby

and Jalal Fadili Ecole Nationale Supérieure d’Ingénieurs de Caen, France

This thoroughly updated edition presents state-of-the-art sparse and multiscale image and signal processing with applications in astronomy, biology, physics, MRI, digital media, and forensics. New chapters and sections cover dictionary learning, 3-D data (data cubes), and geo-located data. MATLAB® and IDL code are also available. Review of previous edition: ‘One of the main virtues of this book is the expert insight that the authors provide into several design and algorithmic choices that one can face when solving practical problems. The authors give some guidance into understanding how sparsity helps in signal and image processing, what some benefits of overcomplete representations are, when to use isotropic wavelets for image processing, why morphological diversity can be helpful, and how to choose between analysis and synthesis priors for regularization in inverse problems.’ Michael B. Wakin, IEEE Signal Processing Magazine 2015 253 x 177 mm 428pp 194 b/w illus. 109 colour illus. 8 tables 978-1-107-08806-1 Hardback £54.99 / US$89.99 For all formats available, see

www.cambridge.org/9781107088061

Textbook

Machine Learning Refined Foundations, Algorithms, and Applications Jeremy Watt Northwestern University, Illinois

Reza Borhani Northwestern University, Illinois

and Aggelos K. Katsaggelos Northwestern University, Illinois

This book provides a fresh, intuitive approach to machine learning, detailing the fundamental concepts necessary for building projects and conducting research. With colour illustrations,

Computer Science / Physical Sciences and Engineering practical real-world examples, and MATLAB-based exercises, it is an essential resource for students and an ideal reference for researchers and practitioners in the field. Contents: 1. Introduction; Part I. The Basics: 2. Fundamentals of numerical optimization; 3. Knowledge-driven regression; 4. Knowledge-driven classification; Part II. Automatic Feature Design: 5. Automatic feature design for regression; 6. Automatic feature design for classification; 7. Kernels, backpropagation, and regularized cross-validation; Part III. Tools for Large Scale Data: 8. Advanced gradient schemes; 9. Dimension reduction techniques; Part IV. Appendices. 2016 247 x 174 mm 298pp 135 colour illus. 3 tables 81 exercises 978-1-107-12352-6 Hardback £54.99 / US$84.99 For all formats available, see

www.cambridge.org/9781107123526

Physical Sciences and Engineering

2017 247 x 174 mm 320pp 85 b/w illus. 6 tables 978-1-316-64221-4 Paperback £29.99 / US$37.99

2016 253 x 177 mm 632pp 182 b/w illus. 15 colour illus. 978-1-107-11561-3 Hardback £54.99 / US$89.99

For all formats available, see

www.cambridge.org/9781316642214

www.cambridge.org/9781107115613

Bayesian Models for Astrophysical Data Arizona State University

Rafael S. de Souza

Francesco Borrelli

Eötvös Loránd University, Budapest

and Emille E. O. Ishida Université Clermont-Auvergne (Université Blaise Pascal), France

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or adapt. A must-have for astronomers, its concrete focus on modeling, analysis, and interpretation will also be attractive to researchers in the sciences more broadly. 2017 253 x 177 mm 424pp 66 b/w illus. 23 colour illus. 11 tables 978-1-107-13308-2 Hardback £60.00 / US$75.00 For all formats available, see

Practical Bayesian Inference A Primer for Physical Scientists Coryn A. L. Bailer-Jones Max-Planck-Institut für Astronomie, Heidelberg

This volume introduces the major concepts of probability and statistics and the computational tools students need to extract information from data in the presence of uncertainty. Using a simple and intuitive Bayesian approach, the emphasis throughout is on the principles and showing how these methods can be implemented in practice. ‘Coryn A. L. Bailer-Jones provides a coherent introduction to the most important modern statistical methods and computational tools for analysing data. His writing style is easy to follow, without the burden of formal proofs and complex derivations, but with sufficient mathematical rigour. This book could be used as an excellent textbook for a semesterlong course aimed at undergraduate and graduate students of physical sciences and engineering (knowledge of basic calculus is assumed, but no specific experience with probability or statistics is required). Theoretical concepts and examples of applications are extensively illustrated and supported by author’s code in the R language.’

Textbook

Predictive Control for Linear and Hybrid Systems

Using R, JAGS, Python, and Stan Joseph M. Hilbe

www.cambridge.org/9781107133082

Numerical Analysis Using R Solutions to ODEs and PDEs Graham W. Griffiths City University London

This book presents the latest numerical solutions to initial value problems and boundary value problems described by ODEs and PDEs. The author offers practical methods for a wide range of problems and illustrates them in the increasingly popular open source language R, allowing integration with more statistical methods. ‘Graham W. Griffiths has produced an outstanding contribution to scientific computation, specifically, the numerical solution of a series of real-world ODE/PDE models. The format of each chapter, i.e. a detailed discussion of the origin of each model, a listing of the commented R routines with background for the numerical algorithms, and an analysis of the computed solutions, permits the reader to immediately understand and execute each model.’

University of California, Berkeley

Alberto Bemporad IMT Institute for Advanced Studies, Italy

and Manfred Morari Swiss Federal Institute of Technology (ETH), Zűrich

With a simple, unified approach, and with consideration of real-time applications, this book covers the theory of stability, feasibility, and robustness of model predictive control (MPC). It is for graduate and postgraduate students, as well as advanced control practitioners interested in the theory and/or implementation of predictive control. Contents: Preface; Acknowledgements; Symbols and acronyms; Part I. Basics of Optimization: 1. Main concepts; 2. Linear and quadratic optimization; 3. Numerical methods for optimization; 4. Polyhedra and p-collections; Part II. Multiparametric Programming: 5. Multiparametric nonlinear programming; 6. Multiparametric programming: a geometric approach; Part III. Optimal Control: 7. General formulation and discussion; 8. Linear quadratic optimal control; 9. Linear 1/∞ norm optimal control; Part IV. Constrained Optimal Control of Linear Systems: 10. Controllability, reachability and invariance; 11. Constrained optimal control; 12. Receding horizon control; 13. Approximate receding horizon control; 14. On-line control computation; 15. Constrained robust optimal control; Part V. Constrained Optimal Control of Hybrid Systems: 16. Models of hybrid systems; 17. Optimal control of hybrid systems; References; Index. 2017 246 x 189 mm 458pp 116 b/w illus. 11 tables 978-1-107-01688-0 Hardback £120.00 / US$150.00 978-1-107-65287-3 Paperback £51.99 / US$64.99 Publication May 2017 For all formats available, see

www.cambridge.org/9781107016880

W. E. Schiesser, Lehigh University, Pennsylvania

Željko Ivezić, University of Washington

For regular email alerts visit www.cambridge.org/alerts

Physical Sciences and Engineering Textbook

Partially Observed Markov Decision Processes From Filtering to Controlled Sensing Vikram Krishnamurthy University of British Columbia

This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, whilst linking theory to realworld applications in controlled sensing. Computations are kept to a minimum, enabling students and researchers in engineering, operations research, and economics to understand the methods and determine the structure of their optimal solution. Contents: Preface; 1. Introduction; Part I. Stochastic Models and Bayesian Filtering: 2. Stochastic state-space models; 3. Optimal filtering; 4. Algorithms for maximum likelihood parameter estimation; 5. Multi-agent sensing: social learning and data incest; Part II. Partially Observed Markov Decision Processes. Models and Algorithms: 6. Fully observed Markov decision processes; 7. Partially observed Markov decision processes (POMDPs); 8. POMDPs in controlled sensing and sensor scheduling; Part III. Partially Observed Markov Decision Processes: 9. Structural results for Markov decision processes; 10. Structural results for optimal filters; 11. Monotonicity of value function for POMPDs; 12. Structural results for stopping time POMPDs; 13. Stopping time POMPDs for quickest change detection; 14. Myopic policy bounds for POMPDs and sensitivity to model parameters; Part IV. Stochastic Approximation and Reinforcement Learning: 15. Stochastic optimization and gradient estimation; 16. Reinforcement learning; 17. Stochastic approximation algorithms: examples; 18. Summary of algorithms for solving POMPDs; Appendix A. Short primer on stochastic simulation; Appendix B. Continuous-time HMM filters; Appendix C. Markov processes; Appendix D. Some limit theorems; Bibliography; Index. 2016 247 x 174 mm 488pp 47 b/w illus. 5 tables 978-1-107-13460-7 Hardback £59.99 / US$99.99 For all formats available, see

www.cambridge.org/9781107134607

Signal Processing and Networking for Big Data Applications Zhu Han University of Houston

students, scientific researchers and industry practitioners. Contents: Part I. Mathematical Foundations; Part II. Big Data over Cyber Networks; Part III. Big Data over Social Networks; Part IV. Big Data over Biological Networks.

and Dan Wang

2016 247 x 174 mm 457pp 115 b/w illus. 30 tables 978-1-107-09900-5 Hardback £64.99 / US$89.99

Hong Kong Polytechnic University

For all formats available, see

This unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. Covering fundamental signal processing theories, software implementations, and techniques for analysis, design, and optimization, it is ideal for researchers, practitioners, and students in signal processing and communications networks.

www.cambridge.org/9781107099005

Contents: Part I. Overview of Big Data Applications; Part II. Methodology and Mathematical Background; Part III. Big Data Applications.

and Yongxiang Huang

Mingyi Hong Iowa State University

‘A very nice balanced treatment over two large-scale signal processing aspects: mathematical backgrounds versus big data applications, with a strong flavor of distributed optimization and computation.’ Shuguang Cui, University of California, Davis 2017 247 x 174 mm 390pp 91 b/w illus. 11 tables 978-1-107-12438-7 Hardback £99.99 / US$125.00 For all formats available, see

Stochastic Analysis of Scaling Time Series From Turbulence Theory to Applications François G. Schmitt Centre National de la Recherche Scientifique (CNRS), Paris Xiamen University, China

Covering a variety of statistical methods, this book provides readers with a thorough understanding of the techniques used to retrieve multi-scale information from time series data, particularly in relation to turbulence. Case studies and MATLAB codes are supplied, making this an excellent resource for graduate students and researchers. 2016 247 x 174 mm 226pp 148 b/w illus. 978-1-107-06761-5 Hardback £44.99 / US$74.99 For all formats available, see

www.cambridge.org/9781107124387

www.cambridge.org/9781107067615

Big Data over Networks

Spatial Analysis of Coastal Environments

Edited by Shuguang Cui

Sarah M. Hamylton

Texas A & M University

University of Wollongong, New South Wales

Alfred O. Hero, III

This book covers the spatial analytical tools needed to map, monitor and explain or predict coastal features. Presenting empirical geographical approaches using recent technological developments, while providing detailed case studies on a range of coastal environments, it is an ideal resource for undergraduates studying spatial science.

University of Michigan, Ann Arbor

Zhi-Quan Luo University of Minnesota

and José M. F. Moura Carnegie Mellon University, Pennsylvania

Written by experts in the field, this pioneering text is the first to examine the crucial interaction between big data and three diverse networks: communication, social and biological. Using critical mathematical tools and state-of-the-art research results, it is an essential reference for graduate

‘I wish this book had been around when I was a student! It ticks all the boxes: the primary focus on spatial analysis and interrogation of geospatial data is essential for sound, sustainable and evidencebased decision-making, and will give invaluable practical skills to students and practitioners alike; while the adoption of landscape ecology as the underpinning conceptual framework emphasises the need for joined-up, holistic and ultimately spatiallydetermined thinking in coastal science and management. The author shows

Physical Sciences and Engineering / Social and Behavioural Sciences a deep understanding of her subject matter, and her enthusiasm for, and love of, the coast stands out. Even the more complex ideas and methods are explained clearly and in an easily accessible, student-friendly manner. Although written for students of the coast, many of the concepts and methods introduced here will be readily transferrable to other areas of Earth Science specialism where geospatial expertise is needed.’ Darius Bartlett, University College Cork, Ireland 2017 246 x 189 mm 228pp 88 b/w illus. 48 colour illus. 30 tables 978-1-107-07047-9 Hardback £44.99 / US$54.99 For all formats available, see

www.cambridge.org/9781107070479

Social and Behavioural Sciences Introduction to Data Science for Social and Policy Research Collecting and Organizing Data with R and Python Jose Manuel Magallanes Reyes Pontificia Universidad Católica del Perú

Real-world data sets are messy and complicated. This book shows students in social science and public management how to prepare these data sets for analysis, with detailed, step-bystep instructions. R and Python code is introduced gradually, providing readers with the tools they need to work with real data. Contents: Part I. Get Started; Part II. Collect and Clean; Part III. Format and Storage. 2017 228 x 152 mm 250pp 978-1-107-54025-5 Paperback £27.99 / US$34.99 Publication June 2017 For all formats available, see

www.cambridge.org/9781107540255

Textbook

Statistics Using Stata An Integrative Approach Sharon Lawner Weinberg New York University

and Sarah Knapp Abramowitz Drew University, New Jersey

Engaging and accessible, this text closely aligns commands from the popular Stata software package with numerous examples based on real data, enabling students to develop a deep

understanding of statistics in a way that reflects statistical practice. It features a comprehensive coverage of essential topics and extensive online resources. Contents: 1. Introduction; 2. Examining univariate distributions; 3. Measures of location, spread, and skewness; 4. Re-expressing variables; 5. Exploring relationships between two variables; 6. Simple linear regression; 7. Probability fundamentals; 8. Theoretical probability models; 9. The role of sampling in inferential statistics; 10. Inferences involving the mean of a single population when σ is known; 11. Inferences involving the mean when σ is not known: one- and two-sample designs; 12. Research design: introduction and overview; 13. One-way analysis of variance; 14. Two-way analysis of variance; 15. Correlation and simple regression as inferential techniques; 16. An introduction to multiple regression; 17. Nonparametric methods. 2016 253 x 203 mm 640pp 216 b/w illus. 64 tables 420 exercises 978-1-107-46118-5 Paperback £49.99 / US$89.99 For all formats available, see

www.cambridge.org/9781107461185

Highlight Textbook

Statistics Using IBM SPSS An Integrative Approach Third edition Sharon Lawner Weinberg New York University

this book to instructors of a one- or two-semester introductory statistics course.’ Robert W. Lissitz, University of Maryland 2016 253 x 203 mm 592pp 196 b/w illus. 100 tables 417 exercises 978-1-107-46122-2 Paperback £69.99 / US$89.99 For all formats available, see

www.cambridge.org/9781107461222

Multilayer Social Networks Mark E. Dickison Capital One, Virginia

Matteo Magnani Uppsala Universitet, Sweden

and Luca Rossi IT University of Copenhagen, Denmark

Multilayer networks are an emerging and active interdisciplinary area. This book unifies and consolidates existing practical and theoretical knowledge on the topic including data collection and analysis, modeling, and mining of multilayer social network systems, and the evolution of dynamic processes such as information spreading. Contents: Part I. Models and Measures; Part II. Mining Multilayer Networks; Part III. Dynamical Processes; Part IV. Conclusion. 2016 228 x 152 mm 188pp 978-1-107-07949-6 Hardback £59.99 / US$94.99 978-1-107-43875-0 Paperback £24.99 / US$39.99 For all formats available, see

www.cambridge.org/9781107079496

and Sarah Knapp Abramowitz Drew University, New Jersey

Written in a clear and lively tone, Statistics Using IBM SPSS provides a data-centric approach to statistics with integrated SPSS (version 22) commands, ensuring that students gain both a strong conceptual understanding of statistics and practical facility with statistical software. The third edition features a new chapter on research design. ‘This is the third edition of a very popular and useful text. The focus is on using SPSS in the research process. The chapters have illustrative exercises and meaningful real data problem sets that not only make it convenient for teaching but also provide realistic experiences for students that will stay with them for many years. The book does a very good job presenting the challenge of data analysis and the experience of being a serious researcher looking at important problems; it illustrates how a variety of quantitative methods can be applied to real data to tease out and evaluate the inferences suggested by that data. I strongly recommend

Statistical Survey Design and Evaluating Impact Tarun Kumar Roy International Institute for Population Sciences, Mumbai

Rajib Acharya Population Council of India, New Delhi

and Arun Kumar Roy Economic Information Technology, Kolkata

Sample surveys are a popular and viable alternative to official statistics and censuses. This book discusses some important methodologies for developing statistical designs, sample surveys and evaluation designs. Solved examples are included to illustrate each technique covered. 2016 244 x 170 mm 214pp 978-1-107-14645-7 Hardback £54.99 / US$84.99 For all formats available, see

www.cambridge.org/9781107146457

Visit our website at www.cambridge.org/statistics

Social and Behavioural Sciences

Computational Social Science Discovery and Prediction Edited by R. Michael Alvarez California Institute of Technology

This book serves as an introduction to the field of computational social science for academics, students, and practitioners. It will also appeal to data scientists who wish to learn about innovations in the area, in particular those interested in how data analytics is applied to study social behavior. Contents: Part I. Computation Social Science Tools; Part II. Computation Social Science Applications. ‘Computational social science is either the coming or just arrived tidal wave. But how the computations part fits with social science is the most important issue that needs to be settled before this wave overtakes us all. This book does a great job in laying out some of the issues in general terms but, perhaps more importantly, showing the areas where computational social science is (not so) simply good social science.’ Nathaniel Beck, New York University Analytical Methods for Social Research

2016 228 x 152 mm 312pp 46 b/w illus. 2 maps 18 tables 978-1-107-10788-5 Hardback £64.99 / US$99.99 978-1-107-51841-4 Paperback £18.99 / US$34.99 For all formats available, see

www.cambridge.org/9781107107885

Social Signal Processing Edited by Judee K. Burgoon University of Arizona

Nadia Magnenat-Thalmann Université de Genève

Maja Pantic Imperial College London

and Alessandro Vinciarelli University of Glasgow

This book provides authoritative surveys covering all aspects of modeling, detection, analysis, and synthesis of nonverbal behavior in human interaction and human-computer interaction. Each chapter includes both introductory and advanced material to address the needs of beginners and experts. The extensive applications include detection

of developmental diseases and analysis of small groups. Contents: Part I. Conceptual Foundations of Social Signal Processing; Part II. Machine Analysis of Social Signals; Part III. Machine Synthesis of Social Signals; Part IV. Applications of Social Signal Processing. 2017 253 x 177 mm 440pp 20 b/w illus. 10 tables 978-1-107-16126-9 Hardback £69.99 / US$89.99 Publication May 2017 For all formats available, see

www.cambridge.org/9781107161269

Textbook

Collecting Qualitative Data A Practical Guide to Textual, Media and Virtual Techniques Edited by Virginia Braun University of Auckland

Victoria Clarke University of the West of England, Bristol

and Debra Gray University of Winchester

Collecting Qualitative Data provides an accessible guide to moving beyond traditional face-to-face interviews. Focusing on a range of textual, media and virtual methods, and offering both interesting twists on established methods and new techniques for collecting data, the book is an exciting resource for new and experienced qualitative researchers alike. Contents: 1. Collecting textual, media and virtual data in qualitative research; Part I. Textual Data Collection: 2. Short but often sweet: the surprising potential of qualitative survey methods; 3. Once upon a time…: story completion methods; 4. Hypothetically speaking: using vignettes as a stand-alone qualitative method; 5. ‘Coughing everything out’: the solicited diary method; Part II. Media Data Collection: 6. Making media data: an introduction to qualitative media research; 7. ‘God’s great leveller’: talkback radio as qualitative data; 8. Archives of everyday life: using blogs in research; 9. Online discussion forums: a rich and vibrant source of data; Part III. Virtual Data Collection: 10. ‘Type me your answer’: generating interview data via email; 11. A productive chat: instant messaging interviewing; 12. I’m not with you, yet I am … virtual face-to-face interviews; 13. Meeting in virtual spaces: conducting online focus groups. Advance praise: ‘Collecting Qualitative Data is an accessible, informative, and

educational text that brings new life to qualitative methodologies. Edited by leading scholars in the field and including contributions on a diverse range of approaches to qualitative data collection, this book is a must have for anyone who utilises qualitative methods.’ Damien W. Riggs, Flinders University of South Australia 2017 228 x 152 mm 406pp 12 b/w illus. 7 tables 978-1-107-05497-4 Hardback £69.99 / US$110.00 978-1-107-66245-2 Paperback £29.99 / US$44.99 Publication May 2017 For all formats available, see

www.cambridge.org/9781107054974

Also of Interest

Also of Interest Truth or Truthiness Distinguishing Fact from Fiction by Learning to Think Like a Data Scientist Howard Wainer National Board of Medical Examiners, Philadelphia, Pennsylvania

Escaping the clutches of truthiness begins with one simple question: ‘what is the evidence?’ Howard Wainer shows how the sceptical mindset of a data scientist can expose truthiness, nonsense, and outright deception. He evaluates the evidence, or lack thereof, supporting claims in many fields, with special emphasis in education. Contents: Part I. Thinking Like a Data Scientist; Part II. Communicating Like a Data Scientist; Part III. Applying the Tools of Data Science to Education. ‘This book is like the proverbial bag of potato chips. It’s impossible to stop reading after just one of its fun and thought-provoking examples of statistical reasoning.’ Andrew Gelman, Columbia University, New York 2016 228 x 152 mm 232pp 52 b/w illus. 9 colour illus. 12 tables 978-1-107-13057-9 Hardback £19.99 / US$29.99 For all formats available, see

www.cambridge.org/9781107130579

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Logic of Statistical Inference Ian Hacking Preface by Jan-Willem Romeijn

This book showcases Ian Hacking’s early ideas on the central issues surrounding statistical reasoning. Presented in a fresh twenty-first-century series livery, and with a specially commissioned new preface, this influential work is now available for a new generation of readers in statistics, philosophy of science and philosophy of maths. Cambridge Philosophy Classics

2016 228 x 152 mm 226pp 9 b/w illus. 5 tables 978-1-107-14495-8 Hardback £59.99 / US$89.99 978-1-316-50814-5 Paperback £14.99 / US$24.99 For all formats available, see

www.cambridge.org/9781107144958

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Textbook

Rational Decision and Causality

An Introduction to Decision Theory

Ellery Eells

Second edition Martin Peterson

Ellery Eells’ original work examining theories of rational decision making continues to be illuminating for philosophers of science. Presented in a fresh twenty-first-century series livery, and with a specially commissioned new preface, this influential work has been revived for a new generation of readers. Cambridge Philosophy Classics

2016 228 x 152 mm 228pp 7 b/w illus. 14 tables 978-1-107-14481-1 Hardback £59.99 / US$89.99 978-1-316-50795-7 Paperback £14.99 / US$24.99 For all formats available, see

www.cambridge.org/9781107144811

Highlight

Probability and Evidence Paul Horwich

Paul Horwich’s influential work offers resolutions to central issues in the philosophy of science through a probabilistic approach to scientific reasoning and methodology. Presented in a fresh twenty-first-century series livery, with a specially commissioned new preface, it has been revived for a new generation of readers in philosophy of science. Contents: Part I. Methodology; Part II. Probability; Part III. Confirmation; Part IV. Induction; Part V. Prediction; Part VI. Evidence; Part VII. Realism. ‘… the strongest and most influential parts of Probability and Evidence are Horwich’s solutions to various puzzles about scientific reasoning … the book might very well still be of considerable interest to those who are looking for an engaging and readable introduction to the topic of scientific reasoning from a Bayesian perspective.’ Finnur Dellsén, Metascience Cambridge Philosophy Classics

2016 228 x 152 mm 146pp 10 b/w illus. 5 tables 978-1-316-50701-8 Paperback £14.99 / US$24.99

Texas A & M University

An essential introduction to all aspects of decision theory, with new and updated discussions, examples, and exercises. Philosophy students and others will benefit from accessible chapters covering utility theory, risk, Bayesianism, game theory and more. The book is clearly written in non-technical language and includes a glossary of key terms. Contents: Preface; 1. Introduction; 2. The decision matrix; 3. Decisions under ignorance; 4. Decisions under risk; 5. Utility; 6. The mathematics of probability; 7. The philosophy of probability; 8. Bayesianism and pragmatic arguments; 9. Causal vs evidential decision theory; 10. Risk aversion; 11. Game theory I: basic concepts and zero-sum games; 12. Game theory II: nonzero-sum and cooperative games; 13. Social choice theory; 14. Overview of descriptive decision theory; Appendix A. Glossary; Further reading; Index. Cambridge Introductions to Philosophy

2017 247 x 174 mm 348pp 11 b/w illus. 97 tables 978-1-107-15159-8 Hardback £69.99 / US$87.99 978-1-316-60620-9 Paperback £24.99 / US$31.99 For all formats available, see

www.cambridge.org/9781107151598

Teaching Probability Jenny Gage and David Spiegelhalter

Written by leading subject specialists, Teaching Probability is designed to support teaching concepts in probability by providing a new approach to this difficult subject from a perspective not limited by a syllabus, giving teachers both theoretical and practical knowledge of an innovative way of teaching probability. 2016 244 x 170 mm 206pp 978-1-316-60589-9 Paperback £17.95 / US$29.38 For all formats available, see

www.cambridge.org/9781316605899

For all formats available, see

www.cambridge.org/9781316507018

eBooks available at www.cambridge.org/ebookstore

Index

Abergel, Frédéric....................................12 Abramowitz, Sarah Knapp......................17 Acharya, Rajib........................................17 Actuarial Mathematics for Life Contingent Risks.................................10 Agarwal, Deepak K.................................14 Algorithms and Models for Network Data and Link Analysis.........................14 Alvarez, R. Michael.................................18 Anane, Marouane..................................12 Appasani, Krishnarao.............................13 Applied Conic Finance............................11

Eells, Ellery.............................................19 Efron, Bradley................................... 5, 1, 4 Elements of Financial Econometrics, The.. 11 Experimental Design for Laboratory Biologists............................................12

B Bailer-Jones, Coryn A. L...........................15 Barabási, Albert-László.............................3 Barlow, Martin T.......................................7 Bayesian Models for Astrophysical Data..15 Bellhouse, David R....................................9 Bemporad, Alberto.................................15 Bennett, Mark J......................................12 Berman, Nancy Greene...........................12 Bichler, Martin........................................11 Big Data over Networks..........................16 Bioinformatics and Computational Biology in Drug Discovery and Development.......................................13 Bishop, Christopher J................................7 Bobrowski, Adam.....................................8 Borhani, Reza.........................................14 Borodin, Alexei.........................................8 Borrelli, Francesco..................................15 Bovier, Anton............................................8 Braun, Virginia.......................................18 Braun, W. John.........................................2 Burgoon, Judee K...................................18

C Capiński, Marek.....................................11 Case-Control Studies................................3 Cause and Correlation in Biology............13 Ceccherini-Silberstein, Tullio......................8 Chakraborti, Anirban..............................12 Chen, Bee-Chung...................................14 Clarke, Victoria.......................................18 Collecting Qualitative Data.....................18 Computational Social Science.................18 Computer Age Statistical Inference....... 1, 4 Confidence, Likelihood, Probability............6 Contest Theory.......................................12 Convergence of One-Parameter Operator Semigroups.............................8 Cox, D. R..................................................3 Cox, David...............................................4 Credit Risk.............................................11 Cui, Shuguang.......................................16

D Data Management Essentials Using SAS and JMP................................................2 de Jong, Piet..........................................10 de Souza, Rafael S..................................15 Derrig, Richard A......................................9 Dickison, Mark E....................................17 Dickson, David C. M........................... 9, 10 Donnelly, Christl A....................................3

F Fadili, Jalal.............................................14 Fan, Jianqing..........................................11 Financial Analytics with R.......................12 Financial Enterprise Risk Management......9 First Course in Statistical Programming with R, A...............................................2 Fountoulakis, Nikolaos..............................8 Fouss, François.......................................14 Fractals in Probability and Analysis............7 Franz, Uwe...............................................8 Frees, Edward W................................. 9, 10 Friedli, Sacha............................................3 Fundamentals of Nonparametric Bayesian Inference................................5

G Gage, Jenny...........................................19 Gaussian Processes on Trees.....................8 Generalized Linear Models for Insurance Data....................................................10 Genome-Wide Association Studies..........13 Ghosal, Subhashis....................................5 Ghosh, Debashis....................................13 Giné, Evarist.............................................6 Goeree, Jacob K......................................11 Graphical Models for Categorical Data......6 Gray, Debra............................................18 Griffiths, Graham W................................15 Groups, Graphs and Random Walks..........8

H Hacking, Ian...........................................18 Hamylton, Sarah M.................................16 Han, Zhu................................................16 Hand, D. J.................................................4 Handbook for Applied Modeling: NonGaussian and Correlated Data...............5 Handbook of Spectrum Auction Design...11 Hardy, Mary R........................................10 Hastie, Trevor....................................... 1, 4 Hefetz, Dan..............................................8 Heller, Gillian Z.......................................10 Hero, III, Alfred O....................................16 Herzberg, A. M.........................................4 Hilbe, Joseph M......................................15 Hill, Melissa.............................................2 Hjort, Nils Lid...........................................6 Hofstad, Remco van der............................1 Hong, Mingyi.........................................16 Horwich, Paul.........................................19 Huang, Yongxiang..................................16 Hugen, Dirk L.........................................12

I Insurance Risk and Ruin...........................9 Integrating Omics Data...........................13 Introduction to Data Science for Social and Policy Research.............................17 Introduction to Decision Theory, An.........19

Introduction to the Theory of Reproducing Kernel Hilbert Spaces, An...8 Ishida, Emille E. O...................................15

J Jedidi, Aymen.........................................12

K Katsaggelos, Aggelos K...........................14 Keogh, Ruth H..........................................3 Kezik, Julie...............................................2 Kolaczyk, Eric D........................................6 Krishnamurthy, Vikram............................16 Krivelevich, Michael..................................8

L Lalonde, Trent...........................................5 Large-Scale Inference...............................5 Lazic, Stanley E.......................................12 Leases for Lives........................................9 Limit Order Books...................................12 Logic of Statistical Inference...................18 Loging, William T....................................13 Long-Range Dependence and SelfSimilarity...............................................7 Loss Coverage........................................11 Luo, Zhi-Quan........................................16 Lyons, Russell...........................................2

M Machine Learning Refined......................14 Madan, Dilip..........................................11 Magallanes Reyes, Jose Manuel..............17 Magnani, Matteo...................................17 Magnenat-Thalmann, Nadia...................18 Mathematical Foundations of InfiniteDimensional Statistical Models...............6 Matsumoto, Hiroyuki................................7 McDiarmid, Colin.....................................8 Measures, Integrals and Martingales.........6 Menshikov, Mikhail..................................7 Meyers, Glenn..........................................9 Mitzenmacher, Michael...........................14 Morari, Manfred.....................................15 Moura, José M. F....................................16 Multilayer Social Networks.....................17 Muni Toke, Ioane....................................12 Murdoch, Duncan J...................................2 Murtagh, Fionn......................................14

N Network Science......................................3 Nickl, Richard...........................................6 Non-homogeneous Random Walks...........7 Nonlife Actuarial Models........................10 Numerical Analysis Using R.....................15

O Olshanski, Grigori.....................................8

P Panagiotou, Konstantinos.........................8 Pantic, Maja...........................................18 Parker, Robert A......................................12 Partially Observed Markov Decision Processes............................................16

Index Paulsen, Vern I..........................................8 Penrose, Mathew.....................................8 Peres, Yuval.......................................... 2, 7 Peterson, Martin.....................................19 Pipiras, Vladas..........................................7 Planning Clinical Research......................12 Popov, Serguei.........................................7 Pósfai, Márton.........................................3 Practical Bayesian Inference....................15 Predictive Control for Linear and Hybrid Systems...............................................15 Predictive Modeling Applications in Actuarial Science...................................9 Principles of Applied Statistics...................3 Principles of Statistical Inference...............3 Privault, Nicolas.......................................8 Probability and Computing.....................14 Probability and Evidence.........................19 Probability on Real Lie Algebras................8 Probability on Trees and Networks............2

R Raghupathi, Mrinal..................................8 Random Graphs and Complex Networks...1 Random Graphs, Geometry and Asymptotic Structure.............................8 Random Walks and Heat Kernels on Graphs..................................................7 Rational Decision and Causality..............19 Regression Modeling with Actuarial and Financial Applications..........................10 Representations of the Infinite Symmetric Group...................................8 Riggs, Jamie.............................................5 Romeijn, Jan-Willem...............................18 Rossi, Luca.............................................17 Roughgarden, Tim..................................11 Roverato, Alberto.....................................6 Roy, Arun Kumar....................................17

Roy, Tarun Kumar...................................17

S Saerens, Marco.......................................14 Salvatori, Maura.......................................8 Sava-Huss, Ecaterina................................8 Scherer, Stephen W.................................13 Schilling, René L.......................................6 Schmitt, François G.................................16 Schoutens, Wim.....................................11 Schweder, Tore.........................................6 Selected Statistical Papers of Sir David Cox.......................................................4 Shimbo, Masashi....................................14 Shipley, Bill.............................................13 Signal Processing and Networking for Big Data Applications..........................16 Social Signal Processing..........................18 Solutions Manual for Actuarial Mathematics for Life Contingent Risks.10 Sparse Image and Signal Processing.......14 Spatial Analysis of Coastal Environments.16 Spiegelhalter, David................................19 Starck, Jean-Luc.....................................14 Statistical Mechanics of Lattice Systems....3 Statistical Methods for Recommender Systems...............................................14 Statistical Survey Design and Evaluating Impact................................................17 Statistics Using IBM SPSS.......................17 Statistics Using Stata..............................17 Stochastic Analysis...................................7 Stochastic Analysis of Scaling Time Series..................................................16 Sweeting, Paul.........................................9

Teaching Probability...............................19 Thomas, Guy..........................................11 Topics at the Frontier of Statistics and Network Analysis...................................6 Truth or Truthiness..................................18 Tse, Yiu-Kuen..........................................10 Tseng, George........................................13 Twenty Lectures on Algorithmic Game Theory.................................................11

U Upfal, Eli................................................14

V van der Vaart, Aad....................................5 Velenik, Yvan............................................3 Vinciarelli, Alessandro.............................18 Visscher, Peter M....................................13 Vojnović, Milan......................................12

W Wade, Andrew..........................................7 Wainer, Howard......................................18 Wang, Dan.............................................16 Waters, Howard R...................................10 Watt, Jeremy..........................................14 Weinberg, Sharon Lawner.......................17

Y Yao, Qiwei..............................................11

Z Zastawniak, Tomasz...............................11 Zhou, Xianghong Jasmine.......................13

Taniguchi, Setsuo.....................................7 Taqqu, Murad S........................................7

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