Statistics Courses

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New and Noteworthy Textbooks for Your

STATISTICS COURSES

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Contents Introductory Statistics and General References ......3 Statistical Theory and Methods..............................4 Computational Statistics ......................................10 Biostatistics ..........................................................12 Page 3

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Statistical Genetics and Bioinformatics ................14 Statistics for Engineering and Physical Science ....15 Statistics for Finance ............................................17 Statistics for Biological Sciences ..........................21 Statistics for Social Science and Psychology ........22

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For more information and complete tables of contents or to *request your complimentary eBook or print exam copy visit

www.crctextbooks.com 1-800-634-7064 • orders@crcpress.com

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Introductory Statistics and General References Introduction to Probability with Texas Hold’em Examples

Introduction to the Theory of Statistical Inference

Frederic Paik Schoenberg

University of Potsdam, Germany

University of California, Los Angeles, USA

“… the laserlike focus of the examples and exercises sets this book apart from other probability textbooks at this level. … The book is incredibly well researched — examples are drawn from actual televised poker games, and many explorations of the probabilities in play in a given game situation conclude with a sentence about what really happened, which is a nice touch.”

—Mark Bollman, MAA Reviews, February 2012

This classroom-tested book illustrates both standard and advanced probability topics using Texas Hold’em, rather than the typical balls in urns. The author includes examples of actual hands of Texas Hold’em from the World Series of Poker and other major tournaments. A dedicated R package that simulates hands and tournaments is freely available from CRAN. • Uses real examples of Texas Hold’em hands to illustrate various probability topics • Discusses random walks and the arcsine laws • Shows students how to use R to compute/approximate probabilities and simulate Texas Hold’em hands or tournaments • Covers important poker concepts, such as pot and implied odds, the “fundamental theorem of poker,” and quantification of luck and skill

Hannelore Liero Silvelyn Zwanzig Uppsala University, Sweden

“… it provides in-depth explanations, complete with proofs, of how statistics works. … The book has several user-friendly aspects. One is the use of eight example data sets to illustrate the theory throughout the text. This repeated use of the same examples allows readers to focus their energy on applying a theoretical point under discussion to a familiar example rather than having to first become acquainted with a new example. Another big help is the detailed solutions provided for the problems that appear at the end of each chapter. … Also helpful: Theoretical or difficult material that can be skipped is marked with an asterisk. … The text analyzes not just methods one learns in a first statistics course, but alternatives as well. Each chapter is capped by a further reading section that is at once comprehensive and concise.”

—David A. Huckaby, MAA Reviews, February 2012

This text presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Unlike related textbooks, it combines the theoretical basis of statistical inference with a useful applied toolbox that includes linear models.

• Presents a concise treatment of the main topics of statistical inference and linear models

• Presents the results of an optimal strategy for simplified poker games

• Includes proofs and solutions to all examples and problems

• Includes exercises in each chapter, with selected solutions in an appendix

• Requires no prior knowledge of measure theory

• Offers a dedicated R package on CRAN that simulates hands and tournaments of Texas Hold’em Figure slides available upon qualifying course adoption

Selected Contents: Probability Basics. Counting Problems. Conditional Probability and Independence. Expected Value and Variance. Discrete Random Variables. Continuous Random Variables. Collections of Random Variables.

Simulation and Approximation Using Computers.

Appendices. References and Suggested Reading.

Index.

• Illustrates theorems and concepts with cartoons and applications to statistical models

Selected Contents: Introduction. Statistical Model. Inference Principles.

Estimation. Testing Hypotheses. Linear Model.

Solutions. Bibliography. Index.

Catalog no. K12437, July 2011, 284 pp., Soft Cover ISBN: 978-1-4398-5292-7, $72.95 / £31.99 Also available as an eBook

Catalog no. K11367, December 2011, 199 pp.

Soft Cover, ISBN: 978-1-4398-2768-0 $49.95 / £31.99 Also available as an eBook

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Intro Stats and General References

A Whistle-Stop Tour of Statistics Brian S. Everitt King’s College, London, UK

This book introduces basic probability and statistics through bite-size coverage of key topics. Designed as a revision aid and study guide, it describes key concepts from probability and statistics in self-contained sections. It also makes an excellent reference for nonstatisticians who need an easy-to-follow reference for basic statistical techniques. The text shows how statistics can be applied in the real world with examples, diagrams, and graphs to illustrate concepts.

Catalog no. K13590, December 2011, 211 pp.

Soft Cover, ISBN: 978-1-4398-7748-7 $41.95 / £26.99

Intro Stats and General References

New!

The R Student Companion Brian Dennis University of Idaho, Moscow, USA

This student-oriented manual describes how to use R in college science and mathematics courses. It features fully developed exercises based around the main precalculus analysis skills needed in the standard college general education courses in science and math.

The author presents applications drawn from all science and social science areas and includes the most often used features of R on a reference card in the back of the book. In addition, each chapter provides a set of exercises of R calculations.

Catalog no. K13498, September 2012, 360 pp.

Soft Cover, ISBN: 978-1-4398-7540-7 $39.95 / £25.99

Statistical Theory and Methods

New!

The A-Z of Error-Free Research Phillip I. Good Consultant, Huntington Beach, California, USA

This practical book begins with an overview of when—and when not—to use statistics. It guides students through the planning and data collection phases and presents various data analysis techniques, including methods for sample size determination. The author then covers techniques for developing models that provide a basis for future research. He also discusses reporting techniques to ensure research efforts get the proper credit. The book concludes with casecontrol and cohort studies. R code is included to implement the methods.

Catalog no. K14287, August 2012, 269 pp.

Soft Cover, ISBN: 978-1-4398-9737-9 $49.95 / £31.99 Also available as an eBook

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Practical Multivariate Analysis Fifth Edition Abdelmonem Afifi, Susanne May, and Virginia A. Clark “I found the text enjoyable and easy to read. The authors provide a sufficient description of all the methodology for practical use. Each chapter includes at least one real-world dataset analysis and the software commands summary tables included at the end of every chapter should be particularly helpful to a practitioner of statistics. … I would recommend the text for practitioners of statistics …”

—Thomas J. Fisher, Journal of Biopharmaceutical Statistics, 2012

Solutions manual available upon qualifying course adoption Catalog no. K10864, July 2011, 537 pp.

ISBN: 978-1-4398-1680-6, $93.95 / £46.99 Also available as an eBook

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Statistical Theory and Methods Coming soon!

Coming soon!

Understanding Advanced Statistical Methods

Statistical Theory

Peter Westfall

Felix Abramovich

Texas Tech University, Lubbock, USA

Tel Aviv University, Israel

Ya' Acov Ritov

Kevin S.S. Henning Sam Houston State University, Huntsville, Texas, USA

The Hebrew University of Jerusalem, Israel

Designed for students in all disciplines—whether social science, biological science, or physical science— this text introduces mathematical statistics, including calculus and probability, in intuitive, self-contained, and accessible ways. Simulations and computing are used throughout. The book discusses Bayesian statistics before frequentist statistics, thoroughly covers populations versus processes, integrates design and measurement with analysis, and emphasizes the understanding and use of statistical models as recipes for producing data.

This text presents a clear introduction to statistical theory for advanced undergraduate students taking a standard course in statistics. It details the main elements and basic concepts of statistical theory, including parameter estimation, confidence intervals, hypothesis testing, Bayesian interference, and decision theory. The book takes an examples-based approach with clear exposition of key topics and just the right amount of mathematical formality. It also includes numerous exercises to enhance students’ understanding of the topics discussed.

• Provides a unique and accessible scientifically based introduction to mathematical statistics

• Presents a clear and concise introduction to the key topics of statistical theory

• Emphasizes simulation and computing with many data examples

• Offers the right balance of exposition and mathematical formality

• Presents the necessary concepts in calculus and probability in a very intuitive way

• Introduces topics with illustrative examples, avoiding a dry approach to the subject

• Includes numerous exercises, enabling use as a course text or for self-study Solutions manual available upon qualifying course adoption

• Includes numerous exercises to facilitate teaching or self-study

Selected Contents: Introduction: Probability, Statistics and Science.

Random Variables and Their Probability Distributions.

Probability Calculation and Simulation. Identifying Distributions. Conditional Distributions and Independence. Marginal Distributions, Joint Distributions, Independence, and Bayes’ Theorem.

Sampling from Populations and Processes. Expected Value and the Law of Large Numbers. Functions of Random Variables: Their Distributions and Expected Values. Distributions of Totals. Estimation: Unbiasedness, Consistency, and Efficiency. The Likelihood Function and Maximum Likelihood Estimates. Bayesian Statistics. Frequentist Statistical Methods. Are Your Results Explainable by Chance Alone? Chi-Squared, Student’s t, and F-Distributions, with Applications. Likelihood Ratio Tests. Sample Size and Power. Robustness and Nonparametric Methods.

A Concise Introduction

Selected Contents: Introduction Parameter Estimation Confidence Intervals and Confidence Regions Large-Sample Theory Hypotheses Testing Bayesian Inference Elementary Decision Theory Catalog no. K12383, April 2013, c. 320 pp.

ISBN: 978-1-4398-5184-5, $49.95 / £31.99 Also available as an eBook

Catalog no. K14873, April 2013, c. 528 pp.

ISBN: 978-1-4665-1210-8, $79.95 / £44.99 Also available as an eBook

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Statistical Theory and Methods New!

Generalized Linear Mixed Models Modern Concepts, Methods and Applications Walter W. Stroup University of Nebraska–Lincoln, USA

This text presents an introduction to linear modeling using the GLMM as an overarching conceptual framework. For students new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider.

• Provides a true introduction to linear modeling • Emphasizes the connection between study design and all aspects of the model • Includes a chapter on GLMM-based power and sample size assessment—a critical tool for costeffective design of research studies • Presents numerous examples using the SAS GLIMMIX procedure • Gives in-depth treatments of issues unique to generalized and mixed linear modeling, including conditional versus marginal modeling, broad versus narrow inference space, and data versus model-scale inference and reporting • Offers the data for all exercises as well as SAS files for all examples at www.crcpress.com

Selected Contents: PART I The Big Picture: Modeling Basics. Design Matters. Setting the Stage. PART II Estimation and Inference Essentials: Estimation. Inference, Part I: Model Effects. Inference, Part II: Covariance Components. PART III Working with GLMMs: Treatment and Explanatory Variable Structure.

Multilevel Models. Best Linear Unbiased Prediction.

Rates and Proportions. Counts. Time-to-Event Data.

Multinomial Data. Correlated Errors, Part I: Repeated Measures. Correlated Errors, Part II: Spatial Variability.

Power, Sample Size, and Planning. Appendices.

References. Index.

Catalog no. K10775, September 2012, 555 pp.

ISBN: 978-1-4398-1512-0, $89.95 / £57.99 Also available as an eBook

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Applied Categorical and Count Data Analysis Wan Tang, Hua He, and Xin M. Tu University of Rochester, New York, USA

This self-contained text explains how to perform the statistical analysis of discrete data. It covers classic concepts and popular topics, along with modern areas that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling students to immediately experiment with the data.

• Shows students how statistical models for noncontinuous responses are applied to real studies, emphasizing difficult and overlooked issues along the pathway from models to data • Covers useful topics in modern-day clinical trials and observation studies • Presents a systematic treatment of instrumentation and measurement models for latent constructs • Compares popular models for clustered data, such as GLMM and GEE/WGEE • Gives an in-depth study of missing values and their impact on parametric and semiparametric (distribution-free) models • Includes exercises at the end of each chapter, many real data examples, and sample programming codes in SAS, SPSS, and STATA for model implementations • Provides codes and updates on a supporting website

Selected Contents: Introduction. Contingency Tables. Sets of Contingency Tables. Regression Models for Categorical Response. Regression Models for Count Response. Loglinear Models for Contingency Tables.

Analyses of Discrete Survival Time. Longitudinal Data Analysis. Evaluation of Instruments. Analysis of Incomplete Data. References. Index.

Catalog no. K10311, June 2012, 384 pp.

ISBN: 978-1-4398-0624-1, $89.95 / £57.99 Also available as an eBook

Request Your Complimentary eBook or Print Exam Copy at

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Statistical Theory and Methods Coming soon!

New!

Stationary Stochastic Processes Theory and Applications Georg Lindgren

Linear Algebra and Matrix Analysis for Statistics Sudipto Banerjee University of Minnesota, Minneapolis, USA

Lund University, Sweden

Anindya Roy

“In many respects, Stationary Stochastic Processes is an updated and expanded version that has captured much of the same spirit (and topics!) as the Cramér and Leadbetter classic. While there have been a number of new and good books published recently on spatial statistics, none cover some of the key important topics such as sample path properties and level crossings in a comprehensive and understandable fashion like Lindgren’s book. This book is required reading for all of my PhD students working in spatial statistics and related areas.”

University of Maryland Baltimore County, USA

—Richard A. Davis, Columbia University

“Particularly appealing features of the book are its numerous examples and remarks (some providing interesting historical background). The structure of the book is such that it can be recommended both as a classroom text and for individual study.”

—Don Percival, University of Washington

“Georg Lindgren’s new book is a most attractive presentation of the theory and application of these processes, with an emphasis on second-order properties and Fourier methods. … it presents, honestly and clearly, all mathematical ideas that are needed, accompanying them by motivation and interpretation that keep the wider purpose in mind. … the book is authoritative and stimulating, a worthy champion of the tradition of Cramér and Leadbetter … a rich, inspiring book, full of good sense and clarity, an outstanding text in this important field.”

—Clive Anderson, University of Sheffield

“This beautifully written text is unlike any other in statistical science. It starts at the level of a first undergraduate course in linear algebra and takes the student all the way up to the graduate level, including Hilbert spaces. It is extremely well crafted and proceeds up through that theory at a very good pace. The statistics chapters are added at just the right places to motivate the reader and illustrate the theory. The book is compactly written and mathematically rigorous, yet the style is lively as well as engaging. This elegant, sophisticated work will serve upper level and graduate statistics education well.

All and all a book I wish I could have written.”

—Jim Zidek, University of British Columbia

• Provides students with an understanding of the major concepts that underlie linear algebra and matrix analysis • Takes a vector-space approach, enabling elegant proofs and a smooth transition to more complex topics • Presents recent developments in fields as diverse as spatial statistics, machine learning, and social network analysis • Requires no prior knowledge of linear algebra • Includes exercises and examples of statistical applications

Selected Contents:

Some Probability and Process Background. Sample Function Properties. Spectral Representations. Linear Filters — General Properties. Linear Filters — Special Topics. Classical Ergodic Theory and Mixing. Vector Processes and Random Fields. Level Crossings and Excursions.

Basic Operations. Systems of Linear Equations. More on Linear Equations. Euclidean Spaces. The Rank of a Matrix. Complementary Subspaces. Orthogonality, Orthogonal Subspaces, and Projections. More on Orthogonality. Revisiting Linear Equations.

Determinants. Eigenvalues and Eigenvectors.

Quadratic Forms. Matrix and Vector Norms. Hilbert Spaces. References.

Catalog no. K15489, October 2012, 375 pp.

ISBN: 978-1-4665-5779-6, $89.95 / £57.99 Also available as an eBook

Catalog no. K10023, July 2013, c. 416 pp.

ISBN: 978-1-4200-9538-8, $79.95 / £49.99 Also available as an eBook

Selected Contents:

For more information and complete contents, visit

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Statistical Theory and Methods

Introduction to Statistical Limit Theory Alan M. Polansky Northern Illinois University, DeKalb, USA

Helping students develop a good understanding of asymptotic theory, this text provides a thorough yet accessible treatment of common modes of convergence and their related tools used in statistics. The author explains as much of the background material as possible and incorporates detailed proofs and explanations of the results. The text includes many end-of-chapter exercises and experiments as well as numerous examples that illustrate the application of asymptotic theory to modern statistical problems. A solutions manual is available upon qualifying course adoption.

Catalog no. C6604, January 2011, 645 pp.

ISBN: 978-1-4200-7660-8, $93.95 / £59.99 Also available as an eBook

Principles of Uncertainty Joseph B. Kadane Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

Winner of the 2011 DeGroot Prize “In this remarkable book, Kadane begins at the most rudimentary level, develops all the needed mathematics on the fly, and still manages to flesh out at least the core of the whole story, slowly, thoughtfully, and rigorously, right up to graduate level. … the author always carefully selects [the theorems] to clarify the basic meaning of the subject and his own views concerning the pitfalls and subtleties of its proper application. Highly recommended.”

—D.V. Feldman, CHOICE, February 2012

Catalog no. K12848, May 2011, 503 pp.

ISBN: 978-1-4398-6161-5, $89.95 / £59.99 Also available as an eBook

Nonparametric Statistical Inference Applied Time Series Analysis Wayne A. Woodward, Henry L. Gray, and Alan C. Elliott “The book contains many illustrative examples, theorems with proofs, and applied and theoretical problems at the end of each chapter with real-life applications. Also, the book looks at generating realisations of the mentioned time series models via software packages such as GW-WINKS and R. The book’s material is very valuable and is well presented, so it represents a good reference at both undergraduate and postgraduate levels, and also a good source for all who are interested in time series analysis.”

—Hassan S. Bakouch, Journal of Applied Statistics, 2012

Catalog no. K10965, October 2011, 564 pp.

ISBN: 978-1-4398-1837-4, $99.95 / £60.99 Also available as an eBook

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Fifth Edition Jean Dickinson Gibbons and Subhabrata Chakraborti University of Alabama, Tuscaloosa, USA

“… one of the best books available for a graduate (or advanced undergraduate) text …”

—Biometrics, September 2011

“This excellently presented book achieves its aim of seeding the fundamentals of non-parametric inference. … The book is undoubtedly well written and presents a good balance of theory and applications. … I would strongly recommend this book to university libraries, teachers and undergraduate students who want to learn non-parametric inference in theory and practice.”

—Journal of the Royal Statistical Society, Series A, April 2011

Catalog no. C7619, July 2010, 650 pp.

ISBN: 978-1-4200-7761-2, $99.95 / £66.99 Also available as an eBook

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Statistical Theory and Methods Introduction to General and Generalized Linear Models Henrik Madsen and Poul Thyregod

Design of Experiments An Introduction Based on Linear Models

Technical University of Denmark, Lyngby

Max Morris

“It is well written, easy to read and the discussion of the examples is clear. As a complement there is a collection of slides for an introductory course on general, generalized, and mixed effects models on the homepage. This book has a good set of references …

I recommend this book as one of the textbooks to be discussed in a course for model building.”

“Overall, this is a book that is easy to like, with good definitions of designs, few typographical errors, and consistent, straightforward explications of the models … I can picture a lot of students using a text aimed at a broad market design course but who need to understand more about what is going on behind the curtain. Morris’ text fills that gap very well.”

—Clarice G.B. Demétrio, Biometrics, February 2012

Iowa State University, Ames, USA

Ancillaries available on the book’s website Catalog no. C9155, November 2010, 316 pp.

ISBN: 978-1-4200-9155-7, $87.95 / £41.99 Also available as an eBook

Bayesian Ideas and Data Analysis An Introduction for Scientists and Statisticians Ronald Christensen, Wesley O. Johnson, Adam J. Branscum, and Timothy E. Hanson “… a very interesting introductory book, very well organised and has been written in a style that is extremely pleasant and enjoyable to read. Both the statistical concepts and examples are very well explained. In conclusion, I highly recommend this book as both a M.S./Ph.D. course text and as an excellent reference book for anyone interested in Bayesian statistics. A copy of it should certainly appear in every university or, even private, library.”

—Rolando de la Cruz, Journal of Applied Statistics, June 2012

Ancillaries available on the book’s website

—Gary W. Oehlert, Biometrics, May 2012

Solutions manual available upon qualifying course adoption Catalog no. C9233, July 2010, 370 pp.

ISBN: 978-1-58488-923-6, $93.95 / £62.99 Also available as an eBook

Time Series Modeling, Computation, and Inference Raquel Prado University of California, Santa Cruz, USA

Mike West Duke University, Durham, North Carolina, USA

“The authors systematically develop a state-of-theart analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.”

—Hsun-Hsien Chang, Computing Reviews, March 2012

Ancillaries available on the book’s website Catalog no. C9336, May 2010, 368 pp.

ISBN: 978-1-4200-9336-0, $99.95 / £62.99 Also available as an eBook

Catalog no. K10199, July 2010, 516 pp.

ISBN: 978-1-4398-0354-7, $72.95 / £48.99 Also available as an eBook

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Computational Statistics New!

R for Statistics

The BUGS Book

Pierre-Andre Cornillon, Arnaud Guyader, Francois Husson, Nicolas Jegou, Julie Josse, Maela Kloareg, Eric Matzner-Lober, and Laurent Rouvière

A Practical Introduction to Bayesian Analysis

“[T]he book is accessible for statisticians of all levels and areas of expertise as well as for novice and advanced R users. … I recommend it for anyone who wants to learn about the why and how of the most commonly employed statistical methods and their extensions.”

—Irina Kukuyeva, Journal of Statistical Software, November 2012

This text explores the use of R for classical statistical analysis. The first half of the book introduces R, data manipulation and visualization, statistical models, graphics, and R programming. The second half presents various statistical analysis techniques by first introducing the data example, then describing the problem to solve, and finally conducting the analysis using R. This example-based approach enables students to replicate the analyses using their own data.

Some of the techniques covered include simple regression, multiple regression, ANOVA, logistic regression, principal component analysis, and clustering.

• Offers a basic introduction to the use of R for statistics • Includes detailed information on installing and getting started with R • Presents material on data visualization, graphics, and programming • Uses an example-based approach to data analysis • Provides code and data sets from CRAN

Selected Contents: An Overview of R: Main Concepts. Preparing Data.

R Graphics. Making Programs with R. Statistical Methods: Introduction to the Statistical Methods. A Quick Start with R. Hypothesis Test. Regression.

Analysis of Variance and Covariance. Classification.

Exploratory Multivariate Analysis. Clustering.

Appendix.

Catalog no. K13834, March 2012, 320 pp.

Soft Cover, ISBN: 978-1-4398-8145-3 $59.95 / £34.99 Also available as an eBook

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David Lunn, Chris Jackson, Nicky Best, Andrew Thomas, and David Spiegelhalter “The most anticipated applied Bayesian text of the last 20 years, The BUGS Book is like a wonderful album by an established rock supergroup: the pressure to deliver a high-quality product was enormous, but the authors have created a masterpiece well worth the wait. The book offers the perfect mix of basic probability calculus, Bayes and MCMC basics, an incredibly broad array of useful statistical models, and a BUGS tutorial and user manual complete with all the ‘tricks’ one would expect from the team that invented the language. BUGS is the dominant Bayesian software package of the post-MCMC era and this book ensures it will remain so for years to come by providing accessible yet comprehensive instruction in its proper use. A must-own for any working applied statistical modeler.”

—Bradley P. Carlin, University of Minnesota

• Provides an accessible, practical introduction to Bayesian analysis using the BUGS software • Covers all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity • Features a large number of worked examples and applications from a wide range of disciplines • Includes detailed exercises and solutions on a supporting website

Selected Contents: Introduction: Probability and Parameters. Monte Carlo Simulations using BUGS. Introduction to Bayesian Inference. Introduction to Markov Chain Monte Carlo Methods. Prior Distributions. Regression Models. Categorical Data. Model Checking and Comparison. Issues in Modeling. Hierarchical Models. Specialized Models. Different Implementations of BUGS. Appendices. Bibliography.

Index.

Catalog no. C8490, October 2012, 399 pp.

Soft Cover, ISBN: 978-1-58488-849-9 $49.95 / £24.99 Also available as an eBook

Request Your Complimentary eBook or Print Exam Copy at

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Computational Statistics Statistical Computing in C++ and R Randall L. Eubank Arizona State University, Tempe, USA

A Gentle Introduction to Stata Revised Third Edition

Ana Kupresanin

Alan C. Acock

Lawrence Livermore National Laboratory, California, USA

Oregon State University, Corvallis, USA

Parallel processing can be ideally suited for the solving of more complex problems in statistical computing.

This book discusses code development in C++ and R, before going beyond to look at the valuable use of these two languages in unison. It covers linear equation solution with regression and linear models motivation, optimization with maximum likelihood and nonlinear least squares motivation, and random number generation. While the text does require a working knowledge of basic concepts in statistics and experience in programming, it does not require knowledge specific to C++ or R.

Updated to reflect the new features of Stata 11, this third edition continues to help new Stata users become proficient in Stata. After reading this introductory text, students will be able to enter, build, and manage a data set as well as perform fundamental statistical analyses. This edition includes a new chapter on the analysis of missing data and the use of multiple-imputation methods. It also provides an extensive revision of the chapter on ANOVA, along with additional material on the application of power analysis.

Each chapter includes exercises and real data sets.

• Integrates both C++ and R for the solution of statistical computing problems

• Shows how to enter, build, and manage a data set

• Covers object-oriented programming in both languages

• Supplements basic statistical modeling topics with discussions of effect sizes and standardized coefficients

• Uses C++ code in R and R functions in the C++ program • Presents applications of the C++ Standard Template Library for statistical computing purposes • Provides an introduction to parallel processing in C++ and R

Selected Contents: Introduction. Computer Representation of Numbers.

A Sketch of C++. Generation of Pseudo-Random Numbers. Programming in R. Creating Classes and Methods in R. Numerical Linear Algebra. Numerical Optimization. Abstract Data Structures. Data Structures in C++. Parallel Computing in C++ and R. An Introduction to Unix. An Introduction to R. C++ Library Extensions (TR1). The Matrix and Vector Classes. The ranGen Class. References. Index.

Catalog no. C6650, December 2011, 556 pp.

ISBN: 978-1-4200-6650-0, $89.95 / £59.99 Also available as an eBook

For more information and complete contents, visit

• Reflects the new features of Stata 11

• Discusses various model selection criteria, such as semipartial correlations • Employs real data sets, such as the General Social Surveys from 2002 and 2006

Selected Contents: Support Materials. Getting Started. Entering Data.

Preparing Data for Analysis. Working with Commands, Do-Files, and Results. Descriptive Statistics and Graphs for One Variable. Statistics and Graphs for Two Categorical Variables. Tests for One or Two Means. Bivariate Correlation and Regression.

Analysis of Variance. Multiple Regression. Logistic Regression. Measurement, Reliability, and Validity.

Working with Missing Values—Multiple Imputation.

Appendix. References. Author Index. Subject Index.

Catalog no. N10594, March 2012, 401 pp.

Soft Cover, ISBN: 978-1-59718-109-9 $79.95 / £49.99

www.crctextbooks.com

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Biostatistics New!

New!

Medical Biostatistics Third Edition Abhaya Indrayan The third edition of this acclaimed text focuses on the statistical aspects of medicine, showing how biostatistics is a useful tool to manage some medical uncertainties. This edition describes several new topics, including adaptive designs, STROBE statement, dietary indices, measures of health inequality, Poisson distribution, path analysis, Six Sigma in health care, and much more. Along with software illustrations, it also expands coverage of survival analysis, ROC curves, equivalence assessment, and repeated measures ANOVA. • Demonstrates how biostatistics can help manage many types of medical uncertainties • Presents step-by-step explanations of statistical methods, along with a large number of real-life examples and worked exercises • Provides guide charts at the beginning of the book to enable quick access of relevant statistical procedure • Illustrates how statistical methods can be used to handle various aspects of a medical research setup

Selected Contents: Medical Uncertainties. Basics of Medical Studies.

Sampling Methods. Designs of Observational Studies. Medical Experiments. Clinical Trials.

Numerical Methods for Representing Variation.

Presentation of Variation by Figures. Some Quantitative Aspects of Medicine. Clinimetrics and Evidence-Based Medicine. Measurement of Community Health. Confidence Intervals, Principles of Tests of Significance, and Sample Size. Inference from Proportions. Relative Risk and Odds Ratio.

Inference from Means. Relationships: Quantitative Data. Relationships: Qualitative Dependent. Survival Analysis. Simultaneous Consideration of Several Variables. Quality Considerations. Statistical Fallacies.

Catalog no. K13952, August 2012, 1024 pp., ISBN: 978-1-4398-8414-0, $129.95 / £82.00 Also available as an eBook

12

Regression Models as a Tool in Medical Research Werner Vach Institute of Medical Biometry and Medical Informatics, Freiburg, Germany

While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the results is often challenging for beginners. This text presents the fundamental concepts and important aspects of regression models most commonly used in medical research, including the classical regression model for continuous outcomes, the logistic regression model for binary outcomes, and the Cox proportional hazards model for survival data. The author emphasizes adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls.

• Helps students improve their understanding of the role of regression models in the medical field • Illustrates each technique with a concrete example, enabling students to better appreciate the properties and theory of the methods • Uses Stata to demonstrate the practical use of the models • Discusses how and when regression models can fail • Describes the basic principles behind statistical computations, with more mathematical details given in the appendices • Offers the data sets, solutions to all exercises, and a short introduction to Stata on the author’s website Figure slides available upon qualifying course adoption

Selected Contents: The Basics. Advanced Topics and Techniques. Risk Scores and Predictors. Miscellaneous. Mathematical Details. Bibliography. Index.

Catalog no. K15111, November 2012, 495 pp.

ISBN: 978-1-4665-1748-6, $89.95 / £57.99 Also available as an eBook

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Biostatistics Multivariate Survival Analysis and Competing Risks

Biostatistics

Martin J. Crowder

Stewart Anderson

Imperial College, University of London, UK

University of Pittsburgh, Pennsylvania, USA

This text introduces univariate survival analysis and extends it to the multivariate case. It also covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods.

Focusing on visualization and computational approaches with an emphasis on the importance of simulation, this book introduces modern and classical biostatistical methods and compares their respective usefulness. Assuming only basic knowledge of probability and statistics, the text covers essential topics in biostatistical science, offers numerous practical applications and detailed worked examples taken from the medical arena (all computed using R and SAS), and includes exercises with solutions.

• Provides a broad overview of multivariate survival analysis, competing risks, and counting processes • Contains many real-world examples to illustrate the methodology

A Computing Approach

Catalog no. C8342, December 2011, 326 pp.

ISBN: 978-1-58488-834-5, $83.95 / £41.99 Also available as an eBook

• Presents a clear style aimed at graduate students in statistics • Offers a supporting R package for the analyses, with some code in the book

Selected Contents: Univariate Survival Analysis: Survival Data. Survival Distributions. Frailty Models. Parametric Methods.

Discrete Time: Non- and Semi-Parametric Methods.

Continuous Time: Non- and Semi-Parametric Methods. Multivariate Survival Analysis: Multivariate Data and Distributions. Frailty and Copulas. Repeated Measure. Wear and Degradation.

Competing Risks: Continuous Failure Times and Their Causes. Parametric Likelihood Inference. Latent Failure Times: Probability Distributions. Discrete Failure Times in Competing Risks. Hazard-Based Methods for Continuous Failure Times. Latent Failure Times: Identifiability Crises. Counting Processes in Survival Analysis: Some Basic Concepts. Survival Analysis. Non- and Semi-Parametric Methods.

Catalog no. K13489, April 2012, 417 pp.

ISBN: 978-1-4398-7521-6, $99.95 / £63.99 Also available as an eBook

Exercises and Solutions in Biostatistical Theory Lawrence L. Kupper, Brian Neelon, and Sean M. O’Brien “Overall, I like this book very much. The problems are carefully chosen and cover a wide range of realworld applications of biostatistical methods.

Instructors and students will find this book to be a good source of supplementary problems for practice. … I have taught courses in mathematical statistics on several prior occasions and wish a book like this was available earlier.”

—Kaushik Ghosh, Journal of Biopharmaceutical Statistics, 2012

Catalog no. C7222, November 2010, 420 pp.

Soft Cover, ISBN: 978-1-58488-722-5 $54.95 / £25.99 Also available as an eBook

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Statistical Genetics and Bioinformatics

Stochastic Modelling for Systems Biology

Statistics and Data Analysis for Microarrays Using R and Bioconductor

Second Edition

Second Edition

Darren J. Wilkinson

Sorin Drăghici

Newcastle University, UK

This text provides an accessible introduction to the use of stochastic processes for modeling biological systems, such as genetic and biochemical networks.

Focusing on simulation, the text includes many examples, R and SBML code, and a number of computerbased exercises.

Fully updated, this second edition includes improvements to the chapters on Markov processes, kinetics, and approximate algorithms. It also greatly expands the coverage of statistical inference using likelihoodfree techniques. Updated computing aspects include a new R package and the use of the new SBML Level 3.

• Provides an accessible introduction to stochastic modeling for systems biology • Focuses on computer simulation, with R and SBML code • Includes many biologically motivated examples

Wayne State University, Detroit, Michigan, USA

This richly illustrated text provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. It takes a hands-on, example-based approach that explains the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems. This updated and expanded edition includes 14 new chapters and offers the R code on a CD-ROM. • Presents an in-depth treatment of the statistical and data analysis aspects used in microarrays and bioinformatics • Provides the option of learning R in parallel with learning about data analysis • Covers background material for those with a limited mathematical, genetic, or molecular biology foundation

• Presents enhanced material on statistical inference

• Includes R code on a CD-ROM

• Contains exercises and further reading in each chapter

Selected Contents:

Selected Contents: Modeling and Networks: Introduction to Biological Modeling. Representation of Biochemical Networks.

Stochastic Processes and Simulation: Probability Models. Stochastic Simulation. Markov Processes.

Stochastic Chemical Kinetics: Chemical and Biochemical Kinetics. Case Studies. Beyond the Gillespie Algorithm. Bayesian Inference: Bayesian Inference and MCMC. Inference for Stochastic Kinetic Models. Conclusions: SBML Models.

Catalog no. K11715, November 2011, 363 pp.

ISBN: 978-1-4398-3772-6, $93.95 / £59.99 Also available as an eBook

The Cell and Its Basic Mechanisms. Microarrays.

Reliability and Reproducibility Issues in DNA Microarray Measurements. Image Processing.

Introduction to R. Bioconductor: Principles and Illustrations. Elements of Statistics. Probability Distributions. Basic Statistics in R. Statistical Hypothesis Testing. Classical Approaches to Data Analysis. ANOVA. Linear Models in R. Experiment Design. Multiple Comparisons. Analysis and Visualization Tools. Cluster Analysis. Quality Control.

Data Pre-Processing and Normalization. Methods for Selecting Differentially Regulated Genes. The Gene Ontology (GO). Functional Analysis and Biological Interpretation of Microarray Data. Uses, Misuses, and Abuses in GO Profiling. A Comparison of Several Tools for Ontological Analysis. Focused Microarrays — Comparison and Selection. ID Mapping Issues.

Pathway Analysis. Machine Learning Techniques. The Road Ahead. References.

Catalog no. K10487, December 2011, 1036 pp.

ISBN: 978-1-4398-0975-4, $89.95 / £57.99 Also available as an eBook

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Statistics for Engineering and Physical Science Probability, Statistics, and Reliability for Engineers and Scientists Third Edition Bilal M. Ayyub and Richard H. McCuen University of Maryland, College Park, USA

The third edition of this bestselling text presents probability, statistics, reliability, and risk methods with an ideal balance of theory and applications. It places increased emphasis on simulation as a modeling tool, applying it progressively with projects that continue in each chapter. This edition also features expanded discussions of ANOVA and a thorough treatment of Monte Carlo simulation. The authors not only establish the limitations, advantages, and disadvantages of each method, but also show that data analysis is a continuum rather than the isolated application of different methods.

• Emphasizes risk and reliability for practical engineering applications • Provides additional material on simulation, the mathematics related to uncertainty, the rand function, sample variability, dependence, the Poisson process, and more • Contains more illustrations on histogram samples and hypothesis testing along with Venn diagrams for conditional probabilities Solutions manual and PowerPoint slides available upon qualifying course adoption

Selected Contents: Introduction. Data Description and Treatment.

Fundamentals of Probability. Probability Distributions for Discrete Random Variables. Probability Distributions for Continuous Random Variables.

Multiple Random Variables. Simulation.

Fundamentals of Statistical Analysis. Hypothesis Testing. Analysis of Variance. Confidence Intervals and Sample Size Determination. Regression Analysis.

Multiple and Nonlinear Regression Analysis.

Reliability Analysis of Components. Reliability and Risk Analysis of Systems. Bayesian Methods.

Appendices. Index.

Catalog no. K10476, April 2011, 663 pp.

ISBN: 978-1-4398-0951-8, $119.95 / £76.99 Also available as an eBook

For more information and complete contents, visit

Applied Reliability Third Edition Paul A. Tobias Retired, Austin, Texas, USA

David C. Trindade Bloom Energy

This popular, easy-to-use guide addresses basic descriptive statistics, reliability concepts, exponential distribution, Weibull distribution, and lognormal distribution. It also covers reliability data plotting, acceleration models, life test data analysis and systems models, and much more. This third edition includes a new chapter on Bayesian reliability analysis as well as expanded, updated coverage of repairable system modeling. Taking a practical and example-oriented approach to reliability analysis, the book provides detailed illustrations of software implementation throughout using several widely available software packages.

• Presents a practical and example-oriented approach suitable for engineering and statistics students • Describes implementation of the methods using statistical software • Emphasizes the powerful methodology of MLE • Provides detailed graphical explanations of methods • Offers software and other files on the book’s CRC Press web page

Selected Contents: Basic Descriptive Statistics. Reliability Concepts.

Exponential Distribution. Weibull Distribution. The Normal and Lognormal Distributions. Reliability Data Plotting. Analysis of Multicensored Data. Physical Acceleration Models. Alternative Reliability Models.

System Failure Modeling: Bottom-Up Approach.

Quality Control in Reliability: Applications of Discrete Distributions. Repairable Systems Part I: Nonparametric Analysis and Renewal Processes.

Repairable Systems Part II: Nonrenewal Processes.

Bayesian Reliability Evaluation. Answers to Selected Exercises. References. Index.

Catalog no. C4665, August 2011, 600 pp.

ISBN: 978-1-58488-466-8, $93.95 / £63.99 Also available as an eBook

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Statistics for Engineering and Physical Science

Probability Foundations for Engineers Joel A. Nachlas Virginia Polytechnic Institute and State University, Blacksburg, USA

“… an excellent introductory book … it will prepare the IE, CE, and EE students for advanced courses that deal with random processes.”

—Edward A. Pohl, University of Arkansas

“… perfect for undergraduate engineering students looking for a textbook on probability.”

—Uday Kumar, Luleå University of Technology

“… an in-depth and rigorous presentation of probability theory … The approach of using everyday engineering intuition to introduce the basic notions of probabilities theory should make this book a valuable tool for engineering students who want to learn the basic concepts and notions of probability theory and to be able to make use of these in engineering problems.”

—Christophe Bereguer, Grenoble Institute of Technology

“… this book takes a fresh approach to teaching undergraduate engineering students the fundamentals of probability. … Students should be better able to retain the knowledge gained through reading this text because of the relevance of the examples and applications.”

—Lisa Maillart, University of Pittsburgh

Solutions manual and PowerPoint slides upon qualifying course adoption

Selected Contents: Historical Perspectives. A Brief Review of Set Theory.

Probability Basics. Random Variables and Distributions. Joint, Marginal, and Conditional Distributions. Expectation and Functions of Random Variables. Moment-Generating Functions.

Approximations and Limiting Behavior. Appendix.

Index.

Catalog no. K14453, May 2012, 184 pp.

ISBN: 978-1-4665-0299-4, $129.95 / £82.00 Also available as an eBook

16

Statistical and Econometric Methods for Transportation Data Analysis Second Edition Simon P. Washington, Matthew G. Karlaftis, and Fred L. Mannering “The second edition introduces an especially broad set of statistical methods, which are useful not only for transportation modeling but also for modeling in other disciplines. … an excellent textbook for advanced undergraduate, master’s, and Ph.D. students, covering topics from simple descriptive statistics to complex Bayesian models. … one of the few books that covers an extensive set of statistical methods needed for data analysis in transportation.”

—Itzhak Ditzian, The American Statistician, November 2011

With many examples and case studies, this update of a bestseller provides an understanding of a broad range of analytical tools required to solve transportation problems. This second edition includes new chapters on logistic regression, ordered probability models, random-parameter models, and Bayesian statistical modeling. Data sets and instructor materials are available on the book’s CRC Press web page.

Selected Contents: FUNDAMENTALS: Statistical Inference I: Descriptive Statistics. Statistical Inference II: Interval Estimation, Hypothesis Testing, and Population Comparisons.

CONTINUOUS DEPENDENT VARIABLE MODELS: Linear Regression. Violations of Regression Assumptions. Simultaneous-Equation Models. Panel Data Analysis. Background and Exploration in Time Series. Forecasting in Time Series: Autoregressive Integrated Moving Average (ARIMA) Models and Extensions. Latent Variable Models. Duration Models.

COUNT AND DISCRETE DEPENDENT VARIABLE MODELS: Count Data Models. Logistic Regression.

Discrete Outcome Models. Ordered Probability Models. Discrete/Continuous Models. OTHER STATISTICAL METHODS: Random-Parameter Models.

Bayesian Models. Appendices. References. Index.

Catalog no. C285X, December 2010, 544 pp.

ISBN: 978-1-4200-8285-2, $104.95 / £69.99 Also available as an eBook

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AZM02_5.5x8.5 TMC_Text 12/18/12 1:56 PM Page 17

Stats for Eng. and Physical Science

Statistics for Finance

New!

Transportation Statistics and Microsimulation

A Course on Statistics for Finance

Clifford Spiegelman, Eun Sug Park, and Laurence R. Rilett

Stanley L. Sclove

By discussing statistical concepts in the context of transportation planning and operations, this text provides the necessary background for making informed transportation-related decisions. It explains the why behind standard methods and uses real-world transportation examples and problems to illustrate key concepts. The book covers the statistical techniques most frequently employed by transportation and pavement professionals.

This text presents statistical methods for financial investment analysis. Providing the connection between elementary statistics courses and quantitative finance courses, the book helps both existing and future quants improve their data analysis skills and better understand the modeling process. It incorporates both applied statistics and mathematical statistics and requires no prior background in finance. The author introduces regression analysis, time series analysis, and multivariate analysis step by step using models and methods from finance.

• Includes realistic transportation-related problems that draw on data from various U.S. transportation studies • Compares planned experiments, quasi-experiments, and field studies • Presents strategies for conducting computeraided statistical designs, fractional factorial designs, and screening designs • Emphasizes bias-corrected confidence intervals • Covers resampling techniques for evaluating uncertainties, including the jackknife and bootstrap

University of Illinois, Chicago, USA

• Incorporates both applied statistics and mathematical statistics • Covers fundamental statistical concepts and tools, including averages, measures of variability, histograms, non-numerical variables, rates of return, and univariate, multivariate, two-way, and seasonal data sets • Presents a careful development of regression, from simple to more complex models • Integrates regression and time series analysis with applications in finance

• Takes a conjugate prior approach to Bayesian estimation

• Requires no prior background in finance

• Discusses smoothing estimators in both regression and density estimation

• Includes many exercises within and at the end of each chapter Figure slides available upon qualifying course adoption

Selected Contents: The Role of Statistics in Transportation Engineering.

Graphical Methods for Displaying Data. Numerical Summary Measures. Probability and Random Variables. Common Probability Distributions.

Sampling Distributions. Inferences: Hypothesis Testing and Interval Estimation. Other Inferential Procedures: ANOVA and Distribution-Free Tests.

Inferences Concerning Categorical Data. Linear Regression. Regression Models for Count Data.

Experimental Design. Cross-Validation, Jackknife, and Bootstrap Methods for Obtaining Standard Errors.

Bayesian Approaches to Transportation Data Analysis.

Microsimulation. Appendix.

Selected Contents: INTRODUCTORY CONCEPTS AND DEFINITIONS: Review of Basic Statistics. Stock Price Series and Rates of Return. Several Stocks and Their Rates of Return.

REGRESSION: Simple Linear Regression; CAPM and Beta. Multiple Regression and Market Models. PORTFOLIO ANALYSIS: Mean-Variance Portfolio Analysis.

Utility-Based Portfolio Analysis. TIME SERIES ANALYSIS: Introduction to Time Series Analysis. Regime Switching Models. Appendices. Index.

Catalog no. K14149, December 2012, 269 pp.

ISBN: 978-1-4398-9254-1, $89.95 / £57.99 Also available as an eBook

Catalog no. K10032, October 2010, 383 pp.

ISBN: 978-1-4398-0023-2, $59.95 / £38.99 Also available as an eBook

For more information and complete contents, visit

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17

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Statistics for Finance

Monte Carlo Simulation with Applications to Finance Hui Wang Brown University, Providence, Rhode Island, USA

An Introduction to Exotic Option Pricing Peter Buchen University of Sydney, Australia

Developed from the author’s course on Monte Carlo simulation at Brown University, this text provides a self-contained introduction to Monte Carlo methods in financial engineering. It covers common variance reduction techniques, the cross-entropy method, and the simulation of diffusion process models. Requiring minimal background in mathematics and finance, the book includes numerous examples of option pricing, risk analysis, and sensitivity analysis as well as many hand-and-paper and MATLAB® coding exercises at the end of every chapter.

In an easy-to-understand, nontechnical yet mathematically elegant manner, this book shows how to price exotic options, including complex ones, without performing complicated integrations or formally solving PDEs. It develops special pricing techniques based on the no-arbitrage principle and fully derives every price formula for the exotic options. The author incorporates a significant amount of original, previously unpublished material and demystifies many esoteric issues underpinning the mathematical treatment of the subject.

• Presents common variance reduction techniques as well as the cross-entropy method

• Fully derives every price formula for the exotic options

• Covers the simulation of diffusion process models

• Develops special pricing techniques based on the no-arbitrage principle

• Requires minimal background in mathematics and finance

• Contains a significant amount of original, previously unpublished material, such as the use of log-volutions and Mellin transforms to solve the Black-Scholes PDE

• Contains numerous examples of option pricing, risk analysis, and sensitivity analysis • Includes many hand-and-paper and MATLAB coding exercises at the end of every chapter

Selected Contents: Review of Probability. Brownian Motion. Arbitrage Free Pricing. Monte Carlo Simulation. Generating Random Variables. Variance Reduction Techniques.

Importance Sampling. Stochastic Calculus.

Simulation of Diffusions. Sensitivity Analysis.

Appendices. Bibliography. Index.

Catalog no. K12713, May 2012, 292 pp.

ISBN: 978-1-4398-5824-0, $79.95 / £49.99 Also available as an eBook

• Demystifies many esoteric issues underpinning the mathematical treatment of the subject • Includes challenging problems at the end of each chapter to illustrate the special pricing techniques Solutions manual available upon qualifying course adoption

Selected Contents: Technical Background: Financial Preliminaries.

Mathematical Preliminaries. Gaussian Random Variables. Applications to Exotic Option Pricing: Simple Exotic Options. Dual Expiry Options. TwoAsset Rainbow Options. Barrier Options. Lookback Options. Asian Options. Exotic Multi-Options.

References. Index.

Catalog no. C9100, February 2012, 296 pp.

ISBN: 978-1-4200-9100-7, $79.95 / £49.99 Also available as an eBook

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AZM02_5.5x8.5 TMC_Text 12/18/12 1:56 PM Page 19

Statistics for Finance Option Valuation

New!

Computational Methods in Finance

A First Course in Financial Mathematics

Ali Hirsa

The George Washington University, Washington, D.C., USA

Caspian Capital Management, LLC, New York, USA

“A natural polymath, the author is at once a teacher, a trader, a quant, and now an author of a book for the ages. The content reflects the author’s vast experience teaching master’s level courses at Columbia and NYU, while simultaneously researching and trading on quantitative finance in leading banks and hedge funds.”

—Peter Carr, Morgan Stanley and NYU Courant Institute of Mathematical Sciences

“A long-time expert in computational finance, Ali Hirsa brings his excellent expository skills to bear on not just one technique but the whole panoply, from finite difference solutions to PDEs/PIDEs through simulation to calibration and parameter estimation.”

—Emanuel Derman, Columbia University and author of Models Behaving Badly

Helping students accurately price a vast array of derivatives, this self-contained text explains how to solve complex functional equations through numerical methods. It addresses key computational methods in finance, including transform techniques, the finite difference method, and Monte Carlo simulation. It also covers model calibration and optimization and describes techniques, such as Kalman and particle filters, for parameter estimation.

Selected Contents: Pricing and Valuation: Stochastic Processes and Risk-Neutral Pricing. Derivatives Pricing via Transform Techniques. Introduction to Finite Differences.

Derivative Pricing via Numerical Solutions of PDEs. Derivative Pricing via Numerical Solutions of PIDEs. Simulation Methods for Derivatives Pricing.

Calibration and Estimation: Model Calibration.

Filtering and Parameter Estimation. References.

Index.

Catalog no. K11454, September 2012, 444 pp.

ISBN: 978-1-4398-2957-8, $89.95 / £59.99 Also available as an eBook

Hugo D. Junghenn

Largely self-contained, this classroom-tested text provides a straightforward introduction to the mathematics and models used in the valuation of financial derivatives. It examines the principles of option pricing in detail via standard binomial and stochastic calculus models and develops the requisite mathematical background as needed. Numerous examples and exercises help students gain expertise with financial calculus methods and increase their general mathematical sophistication.

• Offers a straightforward account of the principles and models of option pricing • Focuses on the (discrete time) binomial model and the (continuous time) Black-Scholes-Merton model • Develops probability theory and finance theory from first principles • Covers various types of financial derivatives, including currency forwards, put and call options, and path-dependent options (Asian, lookback, and barrier options) • Illustrates the similarities and differences between classical calculus and stochastic calculus • Presents a martingale approach to option pricing • Contains many examples and end-of-chapter exercises Solutions manual available upon qualifying course adoption

Selected Contents: Interest and Present Value. Probability Spaces.

Random Variables. Options and Arbitrage. DiscreteTime Portfolio Processes. Expectation of a Random Variable. The Binomial Model. Conditional Expectation and Discrete-Time Martingales. The Binomial Model Revisited. Stochastic Calculus. The Black-Scholes-Merton Model. Continuous-Time Martingales. The BSM Model Revisited. Other Options. Appendices. Bibliography. Index.

Catalog no. K14090, November 2011, 266 pp.

ISBN: 978-1-4398-8911-4, $59.95 / £38.99 Also available as an eBook

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Statistics for Finance Coming soon!

Stochastic Finance

Quantitative Finance

A Numeraire Approach

An Object-Oriented Approach in C++

Jan Vecer

Erik Schlogl

Columbia University, New York, New York, USA

University of Technology, Sydney, Australia

“… this book can be regarded as a wonderful application of stochastic analysis, as it includes not only detailed theoretical proofs but also practical illustrative examples. With the systematic and feasible numeraire techniques, the book can serve as an everyday reference book for practitioners, but also as a powerful tool to deal with pricing and hedging for complicated financial assets. Most importantly, the representation of prices as a pairwise relationship of two assets is the most novel characteristic of this book, which could lead to deeper understanding of derivative contracts.”

This practical textbook builds a foundation in the key methods and models of quantitative finance from the perspective of their implementation in C++. It introduces computational finance in a pragmatic manner, focusing on practical implementation. The author takes an object-oriented approach that starts from simple building blocks for assembling more complex and powerful models. Models and algorithms are expressed in the industry-standard C++ language.

Working C++ source code is available on an accompanying CD-ROM.

—Jian Ping Wan, Mathematical Reviews, 2012f

“Finally, we have a full volume with a systematic treatment of the change of numeraire techniques.

Jan Vecer has taken years of teaching experience and practitioners’ feedback to unify a previously complicated topic into the most elegant and easily accessible numeraire textbook to come down the pike. Now it has become fun to learn about parity and duality relationships among exotic options in a whole variety of models. Practitioners will be happy about the dimension reduction methods. There should be more such books.”

—Uwe Wystup, MathFinance AG

• Presents quantitative finance in a pragmatic manner with a focus on practical implementation • Serves as a self-contained reference for the implementation of the key models and methods • Expresses models and algorithms in the industrystandard programming language C++ • Takes an object-oriented approach, starting from simple building blocks and progressing to more complex and powerful models • Provides working C++ source code on a CD-ROM

Selected Contents:

Introduction. Elements of Finance. Binomial Model.

Diffusion Models. Interest Rate Contracts. Barrier Options. Lookback Options. American Options.

Contracts on Three or More Assets: Quantos, Rainbows and "Friends". Asian Options. Jump Models. Appendix. Solutions to Selected Exercises.

References. Index.

A Brief Review of the C++ Programming Language.

Basic Building Blocks. Portfolio Optimization and Asset Pricing. Lattice Models. The Black-Scholes World. Finite Difference Methods for Partial Differential Equations. Implied Volatility and Implied Distributions. Monte Carlo Simulation. The HeathJarrow-Morton Model. The Lognormal Forward Rate “Market Models.” Case Studies of the ObjectOriented Approach.

Catalog no. K10632, January 2011, 342 pp.

ISBN: 978-1-4398-1250-1, $72.95 / £46.99 Also available as an eBook

Catalog no. C4797, June 2013, c. 506 pp.

ISBN: 978-1-58488-479-8, $79.95 / £49.99 Also available as an eBook

Selected Contents:

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AZM02_5.5x8.5 TMC_Text 12/18/12 1:56 PM Page 21

Statistics for Biological Sciences Introduction to Statistical Data Analysis for the Life Sciences

Modelling and Quantitative Methods in Fisheries

Claus Thorn Ekstrøm and Helle Sørensen

Second Edition

University of Copenhagen, Denmark

CSIRO, Hobart, Tasmania, Australia

“This book can be a valuable assistance for students of life sciences and the other biological faculties and it can be treated both as a first handbook to statistical methods as well as a suitable tool to systematize earlier experiences. … The book is written in a clear and engaging style. The authors put much emphasis on the modelling part of statistical analysis and on biological interpretation of obtained results. It could be recommended for students but also other readers looking for a handbook of ‘practical’ statistics.”

—Ewa Skotarczak, International Statistical Review, 2012

This text provides a computational toolbox that enables students to perform actual analysis for real data sets and gain the confidence and skills to undertake progressively more sophisticated analyses.

Though accessible with any statistical software, the text encourages a reliance on R. The authors provide a short tutorial for those new to the software and include R commands and output at the end of each chapter.

• Uses R software for exercises and examples • Presents exercises with real data sets, which are accessible from the book’s CRC Press web page • Guides students through a proper analysis

Malcolm Haddon

“It is important to remember when reading this book that there are few texts that students can truly consult on fisheries science without a detailed understanding of stock assessment and fisheries management practices—this text continues to bridge that gap. The material has been revised and improvements made to a number of the examples. … The book is lavishly illustrated throughout with the use of Microsoft Excel workbooks, which adds to the flexibility, availability and ease of use of the text. I recommend the text both as a course companion and for private study.”

—Carl M. O’Brien, International Statistical Review, 2012

• Introduces an array of ideas in modeling and quantitative methods that have direct relevance to fisheries science, biological modeling, ecology, and population dynamics • Incorporates two new chapters on characterizing uncertainty and size-based models • Includes many worked examples in Excel that help explain the analyses in detail and demonstrate how to perform the analyses • Provides a set of example workbooks in Excel for download on the book’s CRC Press web page

• Includes solutions to selected exercises

Selected Contents:

Solutions manual available upon qualifying course adoption

Fisheries and Modeling. Simple Population Models.

Model Parameter Estimation. Computer-Intensive Methods. Randomization Tests. Statistical Bootstrap Methods. Monte Carlo Modeling. Characterization of Uncertainty. Growth of Individuals. Stock Recruitment Relationships. Surplus Production Models. Age-Structured Models. Size-Based Models.

Appendix. Bibliography. Index.

Selected Contents: Description of Samples and Populations. Linear Regression. Comparison of Groups. The Normal Distribution. Statistical Models, Estimation, and Confidence Intervals. Hypothesis Tests. Model Validation and Prediction. Linear Normal Models.

Probabilities. The Binomial Distribution. Analysis of Count Data. Logistic Regression. Case Exercises.

Appendices. Bibliography. Index.

Catalog no. C561X, March 2011, 465 pp.

ISBN: 978-1-58488-561-0, $83.95 / £41.99 Also available as an eBook

Catalog no. K11221, August 2010, 427 pp.

Soft Cover, ISBN: 978-1-4398-2555-6 $72.95 / £33.99 Also available as an eBook

For more information and complete contents, visit

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21

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Statistics for Social Science and Psychology

Modern Statistics for the Social and Behavioral Sciences A Practical Introduction Rand Wilcox University of Southern California, Los Angeles, USA

“This is an interesting and valuable book … By gathering a mass of results on that topic into a single volume with references, alternative procedures, and supporting software, the author has provided a valuable service to those interested in these issues, which should probably include anyone teaching the techniques covered in this book. … Recommended to those with a solid background in traditional statistical inference who want a highly competent and comprehensive statement of the cases against traditional statistical inference techniques.”

—Robert W. Hayden, MAA Reviews, March 2012

• Covers standard methods as well as recent advances and insights regarding when classic methods perform well, and when and why they are unsatisfactory • Provides many examples using data from actual studies, which illustrate the potential problems associated with methods routinely taught and used as well as the practical utility of modern techniques.

• Offers over 900 R functions • Includes solutions to selected exercises in an appendix

“Relative advantages/disadvantages of various techniques are presented so that the reader can be helped to understand the choices they make on using the techniques. … A considerable number of illustrations are included and the book focuses on using R for its computer software application. … A useful text for … postgraduate students in the social science disciplines.”

Selected Contents:

—Susan Starkings, International Statistical Review, 2012

Regression and Correlation

Introduction Numerical and Graphical Summaries of Data Probability and Related Concepts Sampling Distributions and Confidence Intervals Hypothesis Testing Bootstrap Methods

Designed for a two-semester, introductory course for graduate students in the social sciences, this text introduces three major insights in the field: 1) sample size estimation needed to justify normality via the central limit theorem, 2) the impact of outliers and heavytailed distributions on power and our ability to obtain an accurate assessment of how groups differ and variables are related, and 3) the deleterious effects of heteroscedasticity on conventional ANOVA and regression methods. Focusing on conceptual issues rather than complex computational details, it also provides a library of R functions for applying modern methods that effectively deal with these insights.

Comparing Two Independent Groups

Requiring no prior training in statistics, the book provides a graduate-level introduction to basic, routinely used statistical techniques relevant to the social and behavioral sciences. It describes methods developed during the last 50 years that deal with known problems associated with classic techniques. Espousing the view that no single method is always best, the text imparts a general understanding of the relative merits of various techniques so that the choice of method can be made in an informed manner.

Tables

22

Comparing Two Dependent Groups One-Way ANOVA Two-Way and Three-Way Designs Comparing More Than Two Dependent Groups Multiple Comparisons Some Multivariate Methods Robust Regression and Measures of Association Basic Methods for Analyzing Categorical Data Answers to Selected Exercises Basic Matrix Algebra References Index

Catalog no. K11557, August 2011, 862 pp.

ISBN: 978-1-4398-3456-5, $93.95 / £52.99 Also available as an eBook

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New from the Chapman & Hall/CRC Texts in Statistical Science Series

With numerous examples using SAS® PROC GLIMMIX, this book is ideal for graduate students in statistics. It focuses on data-driven processes and provides context for extending traditional linear model thinking to generalized linear mixed modeling.

Catalog no. K10775, September 2012, 555 pp.

ISBN: 978-1-4398-1512-0, $89.95 / £57.99 Also available as an eBook

Developed from the authors’ graduate-level biostatistics course, this text explains how to perform the statistical analysis of discrete data. The authors describe the basic ideas underlying each concept, model, and approach to give students a good grasp of the fundamentals of the methodology without using rigorous mathematical arguments.

Catalog no. K10311, June 2012, 384 pp.

ISBN: 978-1-4398-0624-1, $89.95 / £57.99 Also available as an eBook

“… the authors have created a masterpiece well worth the wait. The book offers the perfect mix of basic probability calculus, Bayes and MCMC basics, an incredibly broad array of useful statistical models, and a BUGS tutorial and user manual complete with all the ‘tricks’ one would expect from the team that invented the language. …”

—Bradley P. Carlin, University of Minnesota

Catalog no. C8490, October 2012, 399 pp., Soft Cover, ISBN: 978-1-58488-849-9, $49.95 / £24.99 Also available as an eBook

“This book is a unique blend of classical theory and application theory … The book is very well written, the themes are well chosen and the style is relaxed but precise without being pedantic. My only regret is that this book did not appear earlier! This book is highly recommended!”

—Håvard Rue, Norwegian University of Science and Technology

Catalog no. K15489, October 2012, 375 pp.

ISBN: 978-1-4665-5779-6, $89.95 / £57.99 Also available as an eBook

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