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CONTENTS Introductory Statistics & General References ..........3 Statistical Theory & Methods..................................6 Computational Statistics ......................................13 Biostatistics ............................................................15 Statistics for Engineering & Physical Science ........18 Page 6

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Statistics for Finance..............................................19 Statistics for Biological Sciences ............................22 Statistics for Social Science & Psychology ............22

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Introductory Statistics & General References

New!

Essentials of Multivariate Data Analysis Neil H. Spencer University of Hertfordshire Business School, de Havilland Campus, Hatfield, UK

Since most datasets contain a number of variables, multivariate methods are helpful in answering a variety of research questions. Accessible to students without a substantial background in statistics or mathematics, Essentials of Multivariate Data Analysis explains the usefulness of multivariate methods in applied research.

Unlike most books on multivariate methods, this one makes straightforward analyses easy to perform for students who are unfamiliar with advanced mathematical formulae. An easily understood dataset is used throughout to illustrate the techniques. The accompanying add-in for Microsoft Excel® can be used to carry out the analyses in the text. The dataset and Excel add-in are available for download on the book’s CRC Press web page.

Providing a firm foundation in the most commonly used multivariate techniques, the text helps students choose the appropriate method, learn how to apply it, and understand how to interpret the results. It prepares them for more complex analyses using software such as Minitab®, R, SAS, SPSS, and Stata.

• Explains how multivariate methods are used to address research problems • Covers the most important introductory topics in multivariate statistics, including significance tests, factor analysis, and cluster analysis • Avoids overly complicated formulae for quantitative analysis, making the book accessible to non-statisticians and non-mathematicians • Presents seven different methods for graphically displaying multivariate data • Uses an easily understood dataset to help explain the techniques and an Excel add-in to enable basic analyses, with both available on the book’s CRC Press web page Figure slides available upon qualifying course adoption

Contents: Frequently Asked Questions What Questions?

What Analysis Should I Use? What Data Do I Need?

What Data Is the Author Using in This Book?

What about Missing Data?

What about Other Topics?

What about Computer Packages?

Graphical Presentation of Multivariate Data Why Do I Want to Do Graphical Presentations of Multivariate Data?

What Data Do I Need for Graphical Presentations of Multivariate Data?

The Rest of This Chapter Comparable Histograms A Step-by-Step Guide to Obtaining Comparable Histograms Using the Excel Add-In Multiple Box Plots ...

Multivariate Tests of Significance Why Do I Want to Do Multivariate Tests of Significance?

What Data Do I Need for Multivariate Tests of Significance?

The Rest of This Chapter ...

Factor Analysis Why Do I Want to Do Factor Analysis?

What Data Do I Need for Factor Analysis?

The Rest of This Chapter How Do We Extract the Factors?

Interpreting the Results of a PCA Factor Analysis ...

Cluster Analysis Why Do I Want to Do Cluster Analysis?

What Data Do I Need for Cluster Analysis?

The Rest of This Chapter ...

Discriminant Analysis Why Do I Want to Do Discriminant Analysis?

What Data Do I Need for Discriminant Analysis?

The Rest of This Chapter How Do We Decide How Close a Case Is to Different Groups?

Allocating Individual Cases to Groups ...

Multidimensional Scaling Why Do I Want to Do Multidimensional Scaling?

What Data Do I Need for Multidimensional Scaling?

The Rest of This Chapter ...

Correspondence Analysis Why Do I Want to Do Correspondence Analysis?

What Data Do I Need for Correspondence Analysis?

The Rest of This Chapter ...

References Index Catalog no. K19058, December 2013, 186 pp.

Soft Cover, ISBN: 978-1-4665-8478-5 $59.95 / £34.99 Also available as an eBook

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Introductory Statistics & General References The A–Z of Error-Free Research

The R Student Companion Brian Dennis

Phillip I. Good

University of Idaho, Moscow, USA

Consultant, Huntington Beach, California, USA

“An R book for high schoolers! This is an excellent idea, and the quality of the product is equally excellent. It may be suitable for non-calculus-based introductory courses at the college level as well. … Dennis does a good job dispelling the ‘steep learning curve’ myth concerning R … . The writing style is clear and lively, and the examples should appeal to high school students. It is high time that introductory statistics be taught in an engaging manner that reflects our own enthusiasm for the subject, with meaningful data sets, attractive graphics, and so on. Dennis’ book is a fine contribution toward that goal.”

“Making the transition from student to professional researcher can be a daunting experience. This book can serve as a valuable refresher on hypothesis testing, coping with variation, data collection, sample size decisions and more, along with cursory explanation of R output largely based on freely available data sets. … This is high-level material to aid the reader in becoming a confident researcher … . For the reader who wants to put theory to practice, and do it in R, this work can be a guide to success in analyzing and collection categorical data, detecting confounding, bootstrap approaches, case-control and cohort studies, and more.”

—Tom Schulte, MAA Reviews, April 2013

• Provides a step-by-step prescriptive guide to the data collection, data analysis, design, modeling, and reporting of results involved in clinical trials, experiments, and surveys • Describes contemporary statistical procedures, including bootstrap, decision trees, quantile regression, and permutation tests • Explains how to prepare graphs, tables, and oral presentations • Includes R code to implement the methods, along with a primer on R for students unfamiliar with the software

Selected Contents: Research Essentials. Planning: Hypotheses and Losses. Coping with Variation. Experimental Design.

Data Collection: Fundamentals. Quality Control.

Analyzing Your Data: Describing the Data.

Hypothesis Tests. Multiple Variables and Multiple Tests. Miscellaneous Hypothesis Tests. Sample Size Determination. Building a Model: Ordinary Least Squares. Alternate Regression Methods. Decision Trees. Reporting Your Results: Reports. Oral Presentations. Better Graphics. Nonrandom Samples: Cohort and Case-Control Studies. R Primer.

Bibliography. Indices.

Catalog no. K14287, August 2012, 269 pp.

Soft Cover, ISBN: 978-1-4398-9737-9 $52.95 / £33.99

—Norman Matloff, Journal of Statistical Software, February 2013

• Illustrates how to calculate and graph examples in R using the main topics of precalculus algebra and portions of precalculus statistics • Presents a set of computational exercises in R calculations, which can be performed cooperatively in groups or alone • Assumes only a moderate amount of high school algebra • Approaches R as a comprehensive tool for scientific computing (not just as a statistics package), enabling students to acquire practical skills suitable for all STEM courses

Selected Contents: Introduction: Getting Started with R R Scripts Functions Basic Graphs Data Input and Output Loops Logic and Control Quadratic Functions Trigonometric Functions Exponential and Logarithmic Functions Matrix Arithmetic Systems of Linear Equations Advanced Graphs Probability and Simulation Fitting Models to Data Conclusion—It Doesn’t Take a Rocket Scientist Appendices Index Catalog no. K13498, September 2012, 360 pp.

Soft Cover, ISBN: 978-1-4398-7540-7 $41.95 / £26.99

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

Introduction to the Theory of Statistical Inference

Frederic Paik Schoenberg

Hannelore Liero

University of California, Los Angeles, USA

University of Potsdam, Germany

“… quite entertaining and fun to read. … as a teacher this is definitely a book I would recommend as a pleasant introduction to the world of probability theory.”

Uppsala University, Sweden

Silvelyn Zwanzig

—Julien Sohier, CHANCE, June 2013

“The students will need to understand and have some familiarity with the rules and play of Texas Hold’em. Subject to that, this provides a refreshing new introduction to the subject matter. It is certainly worth considering for your next year’s intake of students.”

—David J. Hand, International Statistical Review (2013), 81, 2

“It is the laserlike focus of the examples and exercises that sets this book apart from other probability textbooks at this level. … The book is incredibly wellresearched—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. It covers basic probability rules, standard models for describing collections of data, and the laws of large numbers as well as more advanced topics, such as the arcsine law and random walks. 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.

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.

Catalog no. K11367, December 2011, 199 pp.

Soft Cover, ISBN: 978-1-4398-2768-0 $52.95 / £33.99 Also available as an eBook

“… it provides in-depth explanations, complete with proofs, of how statistics works. … The book has several user-friendly aspects. … The 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 …

The text analyzes not just methods one learns in a first statistics course, but alternatives as well. …”

—David A. Huckaby, MAA Reviews, February 2012

Catalog no. K12437, July 2011, 284 pp.

Soft Cover, ISBN: 978-1-4398-5292-7 $75.95 / £33.99 Also available as an eBook

A Whistle-Stop Tour of Statistics Brian S. Everitt Professor Emeritus, King's College, London, UK (Retired)

A revision aid and study guide for undergraduate students, this book introduces basic probability and statistics through bite-size coverage of key topics. Using many interesting examples, diagrams, and graphs, it shows students how statistics can be applied in the real world.

Selected Contents: Some Basics and Describing Data. Probability.

Estimation. Inference. Analysis of Variance Models.

Linear Regression Models. Logistic Regression and the Generalized Linear Model. Survival Analysis.

Longitudinal Data and Their Analysis. Multivariate Data and Their Analysis.

Catalog no. K13590, December 2011, 211 pp.

Soft Cover, ISBN: 978-1-4398-7748-7 $43.95 / £28.99

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Statistical Theory & Methods

New!

Bayesian Data Analysis Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin

Praise for the Second Edition

Contents:

“… it is simply the best all-around modern book focused on data analysis currently available. … when students or colleagues ask me which book they need to start with in order to take them as far as possible down the road toward analyzing their own data, Gelman et al. has been my answer since 1995.”

FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models

—Lawrence Joseph, Statistics in Medicine, Vol. 23, 2004

“… easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods.”

—David Blackwell, University of California, Berkeley

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods.

Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.

New to the Third Edition • Four new chapters on nonparametric modeling • Coverage of weakly informative priors and boundary-avoiding priors • Updated discussion of cross-validation and predictive information criteria • Improved convergence monitoring and effective sample size calculations for iterative simulation • Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation • New and revised software code For undergraduate students, the book introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

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FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter.

Catalog no. K11900, November 2013, 675 pp.

ISBN: 978-1-4398-4095-5, $69.95 / £44.99 Also available as an eBook

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Statistical Theory & Methods Data Mining Theories, Algorithms, and Examples Nong Ye “… covers pretty much all the core data mining algorithms. It also covers several useful topics that are not covered by other data mining books, such as univariate and multivariate control charts and wavelet analysis.

Detailed examples are provided to illustrate the practical use of data mining algorithms. A list of software packages is also included for most algorithms covered in the book. … I highly recommend this book for anyone interested in data mining.”

—Jieping Ye, Arizona State University

• Provides theoretical concepts and operational details of data mining algorithms in a selfcontained, complete manner with small data examples • Reviews the necessary mathematical and statistical concepts • Includes exercises in each chapter, a list of software supporting data mining algorithms, and applications of data mining algorithms with references Solutions manual and PowerPoint slides available upon qualifying course adoption

Selected Contents:

New!

Stochastic Modeling and Mathematical Statistics A Text for Statisticians and Quantitative Scientists Francisco J. Samaniego University of California, Davis, USA

This book is intended for a two-quarter or two-semester post-calculus introduction to probability and mathematical statistics for undergraduate students in their junior or senior year. It is also suitable for graduate students in the quantitative sciences, such as agriculture, computer science, ecology, economics, engineering, epidemiology, genetics, psychology, and other areas. The book serves majors and minors in mathematics and statistics as well as students in quantitative disciplines with the appropriate mathematical background and with a serious interest in understanding probability and statistics at the foundational level.

• Emphasizes probability models rather than probability theory • Presents a full treatment of optimality theory for unbiased estimators • Devotes a full chapter to the Bayesian approach to estimation, which includes a final section on comparative statistical inference

AN OVERVIEW OF DATA MINING METHODOLOGIES: Introduction to data mining methodologies.

METHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNS: Regression models.

Bayes classifiers. Decision trees. Multi-layer feedforward artificial neural networks. Support vector machines. Supervised clustering. METHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNS: Hierarchical clustering. Partitional clustering. Self-organized map. Probability distribution estimation. Association rules. Bayesian networks.

METHODOLOGIES FOR MINING DATA REDUCTION PATTERNS: Principal components analysis.

Multi-dimensional scaling. Latent variable analysis.

METHODOLOGIES FOR MINING OUTLIER AND ANOMALY PATTERNS: Univariate control charts.

Multivariate control charts. METHODOLOGIES FOR MINING SEQUENTIAL AND TIME SERIES PATTERNS: Autocorrelation based time series analysis. Hidden Markov models for sequential pattern mining.

Wavelet analysis. Hilbert transform. Nonlinear time series analysis.

Selected Contents:

Catalog no. K10414, July 2013, 349 pp.

ISBN: 978-1-4398-0838-2, $119.95 / £76.99

Catalog no. K15895, January 2014, c. 626 pp.

ISBN: 978-1-4665-6046-8, $89.95 / £57.99

Also available as an eBook

Also available as an eBook

The Calculus of Probability Discrete Probability Models Continuous Probability Models Multivariate Models Limit Theorems and Related Topics Statistical Estimation: Fixed Sample Size Theory Statistical Estimation: Asymptotic Theory Interval Estimation The Bayesian Approach to Estimation Hypothesis Testing Estimation and Testing for Linear Models Nonparametric Statistical Methods Tables Bibliography Index

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Statistical Theory & Methods New!

New!

Nonlinear Time Series

Nonparametric Methods in Statistics with SAS Applications

Theory, Methods, and Applications with R Examples Randal Douc, Eric Moulines, and David Stoffer This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary approaches. They discuss the main limit theorems for Markov chains, useful inequalities, statistical techniques to infer model parameters, and GLMs. Moving on to HMM models, the book examines filtering and smoothing, parametric and nonparametric inference, advanced particle filtering, and numerical methods for inference.

• Describes the major statistical techniques for inferring model parameters, with a focus on the MLE and QMLE • Introduces concepts of nonparametric statistics, including smoothing splines • Covers HMM models, including Gaussian linear, switching Markovian, and nonlinear state space models • Present direct likelihood inference techniques and the EM algorithm • Uses R for numerical examples and provides a dedicated R package Solutions manual available upon qualifying course adoption

Selected Contents: Preliminaries. MARKOV AND ITERATIVE MODELS: Nonlinear Markovian Models. Stability, Recurrence, Mixing. Ergodicity, Limit Theorems. Parametric Inference. Nonparametric Inference. HIDDEN MARKOV MODELS: Some HMM Models. Filtering and Smoothing in HMM. Parametric Inference for HMM. Nonparametric Inference for HMM. Particle Filtering Basics. Advanced Issues in Particle Filtering.

Particle Smoothing Basics. Numerical Methods for Inference.

Catalog no. K14426, December 2013, 551 pp.

ISBN: 978-1-4665-0225-3, $99.95 / £63.99 Also available as an eBook

Olga Korosteleva California State University, Long Beach, USA

This classroom-tested book teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods. Along with exercises at the end of each chapter, the text includes various examples from psychology, education, clinical trials, and other areas.

Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author’s website.

• Covers a variety of nonparametric techniques for hypotheses testing, smoothing, survival analysis, estimation, and more • Contains end-of-chapter exercises and abundant worked examples from the health and social sciences • Includes complete SAS codes for all examples • Provides data sets for exercises on the author’s website Solutions manual and figure slides available upon qualifying course adoption

Selected Contents: Hypotheses Testing for Two Samples: Sign Test for Location Parameter for Matched Paired Samples.

Wilcoxon Signed-Rank Test for Location Parameter for Matched Paired Samples. … Hypotheses Testing for Several Samples: Friedman Rank Test for Location Parameter for Several Dependent Samples.

Kruskal-Wallis H-Test for Location Parameter for Several Independent Samples. Tests for Categorical Data: Spearman Rank Correlation Coefficient Test.

Fisher Exact Test. Nonparametric Regression: Loess Regression. Thin-Plate Smoothing Spline Method.

Nonparametric Generalized Additive Regression: Definition. Nonparametric Binary Logistic Model.

Nonparametric Poisson Model. Time-to-Event Analysis: Kaplan-Meier Estimator of Survival Function. Log-Rank Test for Comparison of Two Survival Functions. Cox Proportional Hazards Model.

Univariate Probability Density Estimation: Histogram. Kernel Density Estimator. Resampling Methods for Interval Estimation: Jackknife.

Bootstrap. Appendices. Recommended Books.

Index of Notation. Index.

Catalog no. K18845, August 2013, 195 pp.

Soft Cover, ISBN: 978-1-4665-8062-6 $69.95 / £44.99 Also available as an eBook

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Statistical Theory & Methods Statistical Methods for Handling Incomplete Data Jae Kwang Kim and Jun Shao Along with many examples, this text covers the most upto-date statistical theories and computational methods for analyzing incomplete data. It presents a thorough treatment of statistical theories of likelihood-based inference with missing data. It also discusses numerous computational techniques and theories on imputation and extensively covers methods involving propensity score weighting, nonignorable missing data, longitudinal missing data, survey sampling, and statistical matching. Some of the research ideas introduced can be developed further for specific applications.

• Uses the mean score equation as a building block for developing the theory for missing data analysis • Provides comprehensive coverage of computational techniques for missing data analysis • Presents a rigorous treatment of imputation techniques, particularly fractional imputation

New!

Stationary Stochastic Processes for Scientists and Engineers Georg Lindgren, Holger Rootzen, and Maria Sandsten “This book is a lucid and well-paced introduction to stationary stochastic processes, superbly motivated and illustrated through a wealth of convincing applications in science and engineering. It offers a clear guide to the formulation and mathematical properties of these processes and to some non-stationary processes too, without going too deeply into the mathematical foundations … . The reader will find tools for analysis and calculation and also— importantly—material to deepen understanding and generate enthusiasm and confidence. An outstanding text.”

—Clive Anderson, University of Sheffield

• Explains the relationship between a covariance function and spectral density • Illustrates the difference between Fourier analysis of data and Fourier transformation of a covariance function

• Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data

• Covers AR, MA, ARMA, and GARCH models

• Describes a survey sampling application

• Shows students how stochastic processes act in linear filters, including the matched, Wiener, and Kalman filters

Selected Contents: Introduction. Likelihood-Based Approach: Observed Likelihood. Mean Score Approach.

Observed Information. Computation. Imputation.

Propensity Scoring Approach: Regression Weighting Method. Propensity Score Method.

Optimal Estimation. Doubly Robust Method.

Empirical Likelihood Method. Nonparametric Method. Nonignorable Missing Data.

Longitudinal and Clustered Data. Application to Survey Sampling: Calibration Estimation. Propensity Score Weighting Method. Fractional Imputation.

Fractional Hot Deck Imputation. Imputation for Two-Phase Sampling. Synthetic Imputation.

Statistical Matching: Instrumental Variable Approach. Measurement Error Models.

Causal Inference. Bibliography. Index.

Catalog no. K12249, July 2013, 223 pp.

ISBN: 978-1-4398-4963-7, $89.95 / £57.99 Also available as an eBook

• Details covariance and spectral estimation

• Describes Monte Carlo simulations of different types of processes • Includes many examples from applied fields as well as exercises that highlight both the theory and practical situations in discrete and continuous time • Provides solutions to exercises and MATLAB® code with examples and data on the first author’s website

Selected Contents: Stochastic Processes. Stationary Processes.

The Poisson Process and Its Relatives. Spectral Representations. Gaussian Processes. Linear Filters— General Theory. AR, MA, and ARMA Models. Linear Filters—Applications. Frequency Analysis and Spectral Estimation. Appendices. References. Index.

Catalog no. K20279, October 2013, 330 pp.

ISBN: 978-1-4665-8618-5, $79.95 / £49.99 Also available as an eBook

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Statistical Theory & Methods Exercises and Solutions in Statistical Theory Lawrence L. Kupper, Brian H. Neelon, and Sean M. O’Brien This book helps students obtain an in-depth understanding of statistical theory by working on and reviewing solutions to interesting and challenging exercises of practical importance. Unlike similar books, this one incorporates many exercises that apply to real-world settings and provides much more thorough solutions. The exercises and selected detailed solutions cover from basic probability theory through to the theory of statistical inference. By mastering the theoretical statistical strategies necessary to solve the exercises, students will be prepared to successfully study even higher-level statistical theory.

• Presents numerous exercises relevant to real-life contexts • Contains very detailed solutions to half of the exercises • Enables instructors to use the material as classroom examples, homework problems, or examination questions • Requires a working understanding of multivariable calculus and basic knowledge of matrices • Provides exercises of varying levels of difficulty, including challenging exercises identified with an asterisk • Offers a detailed summary in the first chapter of all the statistical concepts needed to solve the exercises in the remainder of the book • Includes a section on useful mathematical results Solutions manual available upon qualifying course adoption

Peter Westfall Texas Tech University, Lubbock, USA

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

“The book covers the content of a typical undergraduate math stat text, but with much more thought to application than a typical text. … It would be worth considering for a course using Rice (Mathematical Statistics and Data Analysis). I also recommend it as a reference for anyone teaching applied statistics.”

—Martha K. Smith, The University of Texas at Austin

“This book is unique in the way it approaches this topic. It does not subscribe to the cookbook template of teaching statistics but focuses instead on understanding the distinction between the observed data and the mechanisms that generated it. This focus allows a better distinction between models, parameters, and estimates and should help pave a way to instill statistical thinking to undergraduate students.”

—Mithat Gönen, Memorial Sloan-Kettering Cancer Center

• Shows students how a statistical model is a recipe for producing random data • Provides a self-contained treatment of mathematical statistics • Helps students understand how logical conclusions follow from the assumptions • Teaches Bayesian methods before classical (frequentist) methods • Presents definitions, important formulas, and exercises at the end of every chapter • Gives reasons for each step of the derivations • Offers computer code, sample quizzes, exams, and other supplements on the book’s website Solutions manual available upon qualifying course adoption

Contents: Concepts and Notation: Basic Probability Theory.

Univariate Distribution Theory. Multivariate Distribution Theory. Estimation Theory. Hypothesis Testing Theory. Basic Probability Theory: Exercises.

Solutions to Odd-Numbered Exercises. Univariate Distribution Theory: Exercises. Solutions to OddNumbered Exercises. Multivariate Distribution Theory: Exercises. Solutions to Odd-Numbered Exercises. Estimation Theory: Exercises. Solutions to Odd-Numbered Exercises. Hypothesis Testing Theory: Exercises. Solutions to Odd-Numbered Exercises.

Catalog no. K16626, June 2013, 388 pp.

Soft Cover, ISBN: 978-1-4665-7289-8 $59.95 / £38.99 Also available as an eBook

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Understanding Advanced Statistical Methods

Selected Contents: Introduction. Random Variables and Their Probability Distributions. Probability Calculation and Simulation.

Identifying Distributions. Conditional Distributions and Independence. Marginal Distributions, Joint Distributions, Independence, and Bayes’ Theorem. … 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? …

Catalog no. K14873, April 2013, 569 pp.

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

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Statistical Theory & Methods Statistical Theory A Concise Introduction Felix Abramovich

Stationary Stochastic Processes Theory and Applications

Tel Aviv University, Israel

Georg Lindgren

Ya'acov Ritov

Lund University, Sweden

The Hebrew University of Jerusalem, Israel

“Stationary Stochastic Processes manages to present a wide topic of applied mathematics and does not fall off from the thin ridge that lies between the probabilistic and the more signal process (deterministic) representation of stationary processes. A lot of material can be found therein, and it will be very helpful to young researchers.”

Designed for a one-semester advanced undergraduate or graduate course, this text explains the underlying ideas and principles of major statistical concepts, including parameter estimation, confidence intervals, hypothesis testing, asymptotic analysis, Bayesian inference, and elements of decision theory. It introduces these topics on a clear intuitive level using illustrative examples in addition to the formal definitions, theorems, and proofs. Requiring no heavy calculus, simple questions throughout the text help students check their understanding of the material.

• Delineates the fundamental concepts of statistical theory • Balances exposition and mathematical formality • Introduces topics with numerous examples, avoiding a dry approach to the subject • Assumes an intermediate-level background in calculus and probability • Reviews the necessary probabilistic material in an appendix • Includes a series of exercises of varying levels of difficulty in each chapter, with selected solutions in an appendix

Selected Contents: Introduction. Point Estimation. Confidence Intervals, Bounds, and Regions. Hypothesis Testing. Asymptotic Analysis: Convergence and consistency in MSE. Convergence and consistency in probability. Convergence in distribution. The central limit theorem. Asymptotically normal consistency.

Asymptotic confidence intervals. Asymptotic normality of the MLE. Multiparameter case. Asymptotic distribution of the GLRT. Wilks’ theorem. Bayesian Inference. Elements of Statistical Decision Theory: Risk function and admissibility. Minimax risk and minimax rules. Bayes risk and Bayes rules. Posterior expected loss and Bayes actions. Admissibility and minimaxity of Bayes rules. Linear Models.

Appendices. Index.

—Marc Hoffmann, CHANCE, 26.3

“In many respects, Lindgren’s Stationary Stochastic Processes: Theory and Applications is an updated and expanded version that has captured much of the same spirit (and topics!) as the Cramer 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.”

—Richard A. Davis, Columbia University

• Presents all basic theory together with recent developments from research in the area • Uses a rigorous and application-oriented approach to stationary processes • Explains how the theory is used in applications, such as detection theory, signal processing, spatial statistics, and reliability • Opens the doors to a selection of special topics professors can expand on, including extreme value theory, filter theory, long-range dependence, and point processes

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.

Catalog no. K12383, April 2013, 240 pp.

ISBN: 978-1-4398-5184-5, $69.95 / £44.99

Catalog no. K15489, October 2012, 375 pp.

ISBN: 978-1-4665-5779-6, $93.95 / £59.99

Also available as an eBook

Also available as an eBook

For more information and complete contents, visit www.crctextbooks.com

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Statistical Theory & Methods

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

“Walter Stroup is a leading authority on GLMMs for applied statisticians … . He offers a thorough, engaging, and opinionated treatment of the subject … I found the ‘fully general’ GLMM approach to modeling and design issues (Chapters 1 and 2) to be quite illuminating. … Prospective readers without current access to SAS will be pleased to know that a reasonable level of access to SAS is now available at no cost to students and teachers on the web …”

—Homer White, MAA Reviews, June 2013

Catalog no. K10775, September 2012, 555 pp.

ISBN: 978-1-4398-1512-0, $93.95 / £59.99 Also available as an eBook

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. R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling students to immediately experiment with the data in the examples.

Selected Contents: 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, $93.95 / £59.99

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

Anindya Roy University of Maryland Baltimore County, USA

“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. … The statistics chapters are added at just the right places to motivate the reader and illustrate the theory. … 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

Catalog no. K10023, June 2014, 416 pp.

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

Risk Assessment and Decision Analysis with Bayesian Networks Norman Fenton and Martin Neil Queen Mary University of London, UK

“Risk Assessment and Decision Analysis with Bayesian Networks is a brilliant book. Being a nonmathematician, I’ve found all of the other books on BNs to be an impenetrable mass of mathematical gobble-de-gook. This, in my view, has slowed the uptake of BNs in many disciplines because people simply cannot understand why you would use them and how you can use them. This book finally makes BNs comprehensible, and I plan to develop a risk assessment course at the University of Queensland using this book as the recommended textbook.”

—Carl Smith, The University of Queensland

Catalog no. K10450, November 2012, 524 pp.

ISBN: 978-1-4398-0910-5, $83.95 / £43.99

Also available as an eBook

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Computational Statistics New!

Foundations of Statistical Algorithms With References to R Packages Claus Weihs, Olaf Mersmann, and Uwe Ligges TU Dortmund University, Germany

By reviewing the historical development of basic algorithms, this comprehensive textbook illuminates the evolution of today’s more powerful statistical algorithms. The authors emphasize recurring themes in all statistical algorithms, including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, the text touches on topics not usually covered in similar books, namely, systematic verification and the scaling of many established techniques to very large databases. Broadly accessible, it offers examples, exercises, and selected solutions in each chapter.

• Covers historical developments to clarify the evolution of more powerful statistical algorithms • Emphasizes recurring themes in all statistical algorithms: computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability • Discusses two topics not included in other books: systematic verification and scalability • Contains examples, exercises, and selected solutions in each chapter, with supplementary material available online

Selected Contents:

New!

Probability and Statistics for Computer Scientists Second Edition Michael Baron University of Texas at Dallas, Richardson, USA

Meeting the ABET requirements for probability and statistics, this text helps students understand general methods of stochastic modeling, simulation, and data analysis; make optimal decisions under uncertainty; model and evaluate computer systems and networks; and prepare for advanced probability-based courses.

The second edition offers a new axiomatic introduction of probability, expanded coverage of statistical inference, more exercises at the end of each chapter, and additional MATLAB® codes, particularly new commands of the Statistics Toolbox. The book also includes numerous computer science applications and worked examples.

• Leads students from probability fundamentals to stochastic processes, Markov chains, queuing systems, and Monte Carlo methods—topics frequently missing from standard textbooks • Satisfies the ABET requirements for probability and statistics • Provides MATLAB codes for simulation, computation, and data analysis • Contains many detailed examples that have direct applications to computer science and related areas

Introduction Computation: Models for Computing: What Can a Computer Compute?

Floating-Point Computations: How Does a Computer Compute?

Precision of Computations: How Exact Does a Computer Compute?

Implementation in R Verification. Iteration Deduction of Theoretical Properties: PLS—from Algorithm to Optimality EM Algorithm Implementation in R Randomization Repetition Scalability and Parallelization: Optimization Parallel Computing Implementation in R Bibliography Index

• Summarizes the main concepts at the end of each chapter and reviews the necessary calculus and linear algebra in the appendix

Catalog no. K13688, December 2013, 500 pp.

ISBN: 978-1-4398-7885-9, $79.95 / £38.99

Catalog no. K13525, August 2013, 473 pp.

ISBN: 978-1-4398-7590-2, $99.95 / £63.99

• Presents over 260 exercises for homework assignments and self-training—including 60 new to this edition Solutions manual and figure slides available upon qualifying course adoption

Selected Contents: Introduction and Overview. Probability and Random Variables: Probability. Discrete Random Variables and Their Distributions. Continuous Distributions. Computer Simulations and Monte Carlo Methods. Stochastic Processes: Stochastic Processes. Queuing Systems. Statistics: Introduction to Statistics. Statistical Inference I. Statistical Inference II. Regression. Appendix. Index.

Also available as an eBook

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Computational Statistics R for Statistics Pierre-Andre Cornillon, Arnaud Guyader, Francois Husson, Nicolas Jegou, Julie Josse, Maela Kloareg, Eric Matzner-Lober, and Laurent Rouvière “Section 4.2 on the apply family of functions and related functions for matrices, arrays, and data frames is by far the most friendly and helpful introduction to the subject that I have seen. … this book is a fine place for you to start learning R.” —Homer S. White, MAA Reviews, January 2013

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 $62.95 / £36.99

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

Ana Kupresanin Lawrence Livermore National Laboratory, California, USA

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, $93.95 / £62.99 Also available as an eBook

Also available as an eBook

The BUGS Book A Practical Introduction to Bayesian Analysis

A Gentle Introduction to Stata

David Lunn, Christopher Jackson, Nicky Best, Andrew Thomas, and David Spiegelhalter

Revised Third Edition

“If a book has ever been so much desired in the world of statistics, it is for sure this one. … strikes the right distance between advanced theory and pure practice. … The BUGS Book is not only a major textbook on a topical subject, but it is also a mandatory one for all statisticians willing to learn and analyze data with Bayesian statistics at any level. It will be the companion and reference book for all users (beginners or advanced) of the BUGS software.”

Selected Contents:

—Jean-Louis Fouley, CHANCE, 2013

Catalog no. C8490, October 2012, 399 pp.

Soft Cover, ISBN: 978-1-58488-849-9 $52.95 / £25.99

Alan C. Acock Oregon State University, Corvallis, USA

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

Also available as an eBook

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Biostatistics Survival Analysis in Medicine and Genetics

New!

Epidemiology

National University of Singapore

Study Design and Data Analysis, Third Edition

Shuangge Ma

Mark Woodward

Yale University, New Haven, Connecticut, USA

University of Oxford, UK; University of Sydney, Australia; and Johns Hopkins University, Baltimore, Maryland, USA

Jialiang Li

Using real data sets throughout, this text introduces the latest methods for analyzing high-dimensional survival data. With an emphasis on the applications of survival analysis techniques in genetics, it presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. The book reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.

• Explains how to analyze censored survival time data in medical and genetic research • Provides a pedagogical introduction to time-dependent diagnostic accuracy studies • Covers recent high-dimensional data analysis and variable selection methods • Introduces nonparametric regression for survival analysis and the Fine-Gray model for competing risks data • Includes exercises in each chapter • Offers lecture slides on the book’s CRC Press web page

Selected Contents: Introduction: Examples and Basic Principles.

Analysis Trilogy: Estimation, Test, and Regression.

Analysis of Interval Censored Data. Special Modeling Methodology: Nonparametric Regression. Multivariate Survival Data. Cure Rate Model. Bayesian Analysis. Theoretic Notes.

Diagnostic Medicine for Survival Analysis: Statistics in Diagnostic Medicine. Diagnostics for Survival Outcome under Diverse Censoring Patterns.

Diagnostics for Right Censored Data. Theoretic Notes. Survival Analysis with High-Dimensional Covariates. Bibliography. Index.

Catalog no. K14175, June 2013, 381 pp.

ISBN: 978-1-4398-9311-1, $99.95 / £63.99 Also available as an eBook

Updated and expanded, this popular text focuses on the quantitative aspects of epidemiological research. It shows students how statistical principles and techniques can help solve epidemiological problems.

Along with more exercises and examples using both Stata and SAS, this third edition includes a new chapter on risk scores and clinical decision rules, a new chapter on computer-intensive methods, and new sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines. Supporting materials are available on the book’s CRC Press web page.

• Covers the spectrum of statistical principles and analytical tools used in epidemiological research • Explains how to design epidemiological studies and how to analyze the data from these studies • Uses data sets taken from real epidemiological investigations and publications to illustrate the concepts and methods • Assumes only a basic statistical background, emphasizing practical methods over complicated proofs • Includes extensive references for further reading as well as end-of-chapter exercises to reinforce understanding • Provides data sets, SAS and Stata programs, and more on the book’s CRC Press web page Solutions manual available upon qualifying course adoption

Selected Contents: Fundamental Issues. Basic Analytical Procedures.

Assessing Risk Factors. Confounding and Interaction.

Cohort Studies. Case-Control Studies. Intervention Studies. Sample Size Determination. Modeling Quantitative Outcome Data. Modeling Binary Outcome Data. Modeling Follow-Up Data. MetaAnalysis. Risk Scores and Clinical Decision Rules.

Computer-Intensive Methods. Appendices. Index.

Catalog no. K11828, December 2013, 898 pp.

ISBN: 978-1-4398-3970-6, $99.95 / £49.99 Also available as an eBook

For more information and complete contents, visit www.crctextbooks.com

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Biostatistics Medical Biostatistics Third Edition Abhaya Indrayan The third edition of this acclaimed book focuses on the statistical aspects of medicine, showing how biostatistics is a useful tool to manage many types of medical uncertainties and how statistical methods can be used to handle various aspects of a medical research setup. The author presents step-by-step explanations of statistical methods along with numerous real-world examples and exercises. Guide charts at the beginning of the book enable students to quickly access the relevant statistical procedure.

New to the Third Edition • New topics, including clinical trials with stopping rules, adaptive designs, Likert scale, STROBE statement, graphical and analytical methods for checking Gaussianity, measure of health inequality, Cochran Q test, meta analysis, measures of ordinal association, Dunnett test, an alternative approach for assessing clinical agreement, Six Sigma in health care, and much more • More detailed and expanded coverage of survival analysis • Software illustrations (SPSS) of ANCOVA, repeated measures ANOVA, stepwise regression, quadratic regression. ROC curve (MedCalc), and survival analysis

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, $135.95 / £86.00 Also available as an eBook

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

This text presents the fundamental concepts and important aspects of regression models most commonly used in medical research.

The author emphasizes adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls. This approach helps students improve their understanding of the role of regression models in the medical field.

Each technique is illustrated with a concrete example and Stata is used to demonstrate the practical application of the models. Data sets, solutions to all exercises, and a short introduction to Stata are available on the author’s website. Figure slides are available upon qualifying course adoption.

Selected Contents: THE BASICS: Why Use Regression Models? An Introductory Example. The Classical Multiple Regression Model. Adjusted Effects. Inference for the Classical Multiple Regression Model. Logistic Regression. Inference for the Logistic Regression Model. Categorical Covariates. Handling Ordered Categories: A First Lesson in Regression Modeling Strategies. The Cox Proportional Hazard Model.

Common Pitfalls in Using Regression Models.

ADVANCED TOPICS AND TECHNIQUES: Some Useful Technicalities. Comparing Regression Coefficients. Power and Sample Size. The Selection of the Sample. The Selection of Covariates. Modeling Nonlinear Effects. Transformation of Covariates.

Effect Modification and Interactions. Applying Regression Models to Clustered Data. Applying Regression Models to Longitudinal Data. The Impact of Measurement Error. The Impact of Incomplete Covariate Data. RISK SCORES AND PREDICTORS: Risk Scores. Construction of Predictors. Evaluating the Predictive Performance. Outlook: Construction of Parsimonious Predictors. MISCELLANEOUS: Alternatives to Regression Modeling. Specific Regression Models. Specific Usages of Regression Models. What Is a Good Model? Final Remarks on the Role of Prespecified Models and Model Development. MATHEMATICAL DETAILS. Bibliography. Index.

Catalog no. K15111, November 2012, 495 pp.

ISBN: 978-1-4665-1748-6, $93.95 / £59.99 Also available as an eBook

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Biostatistics Multivariate Survival Analysis and Competing Risks Martin Crowder Imperial College, University of London, UK

Suitable for graduate students and researchers in statistics and biostatistics as well as those in the medical field, epidemiology, and social sciences, this book 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.

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

Biostatistics A Computing Approach Stewart J. Anderson University of Pittsburgh, Pennsylvania, USA

“The book presents important topics in biostatistics alongside examples provided in the programming languages SAS and R. … The book covers many relevant topics every student should know in a way that it makes it easy to follow … each chapter provides exercises encouraging the reader to deepen her/his understanding. I really like that the theory is presented in a clear manner without interruptions of example programs. Instead, the programs are always presented at the end of a section. … this book can serve as a good start for the more statistics inclined students who haven’t yet recognized that in order to become a good biostatistician, you need to be able to write your own code. … I can recommend to all serious students who want to get a thorough start into this field.”

—Frank Emmert-Streib, CHANCE, August 2013

• Presents a clear style aimed at graduate students in statistics

• Provides an introduction to important modern and classical methods used in biostatistics

• Offers a supporting R package for the analyses, with some code in the book

• Focuses on visualization and computational tools

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, $104.95 / £66.99

• Covers key topics in biostatistical science, including linear regression, multivariate regression, and repeated measures • Includes exercises with solutions as well as practical applications and worked examples from the medical area, all computed using R and SAS

Selected Contents: Review of Topics in Probability and Statistics.

Use of Simulation Techniques. The Central Limit Theorem. Correlation and Regression. Analysis of Variance. Discrete Measures of Risk. Multivariate Analysis. Analysis of Repeated Measures Data.

Nonparametric Methods. Analysis of Time to Event Data. Sample Size and Power Calculations.

Appendices. References. Index.

Catalog no. C8342, December 2011, 326 pp.

ISBN: 978-1-58488-834-5, $87.95 / £43.99 Also available as an eBook

Also available as an eBook

For more information and complete contents, visit www.crctextbooks.com

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Statistics for Engineering & Physical Science New!

Introduction to Statistical Process Control Peihua Qiu University of Florida, Gainesville, USA

Second Edition

“Bringing new statistical methods for quality control in line with the computer age, Introduction to Statistical Process Control presents state-of-the-art statistical process control (SPC) techniques for industrial and service processes. This book reflects major progress in the use of SPC for product and process improvement, introduces some of the newest discoveries—and sheds further light on existing ones—on the SPC approaches that can be applied across various areas of research, including engineering, medicine, and service. … It is generally my first recommendation when asked for a valuable resource in the field due to the breadth of topics covered and its practical utility. … an excellent choice as the primary textbook in an SPC course.”

—Changliang Zou, Nankai University

• Explores the major advantages and limitations of traditional and state-of-the-art SPC methods • Offers practical guidelines on implementing the techniques • Examines the most recent research results in various areas, including univariate and multivariate nonparametric SPC, SPC based on change-point detection, and profile monitoring • Keeps the mathematical and statistical prerequisites to a minimum, only requiring basic linear algebra, some calculus, and introductory statistics • Provides more advanced or technical material in discussions at the end of each chapter, along with exercises that encourage hands-on practice with the methods • Presents pseudo codes for important methods • Includes all R functions and datasets on the author’s website

Selected Contents: Basic Statistical Concepts and Methods. Univariate Shewhart Charts and Process Capability. Univariate CUSUM Charts. Univariate EWMA Charts. Univariate Control Charts by Change-Point Detection.

Multivariate Statistical Process Control. Univariate Nonparametric Process Control. Multivariate Nonparametric Process Control. Profile Monitoring.

Appendices. Bibliography. Index.

Catalog no. K12137, October 2013, 520 pp.

ISBN: 978-1-4398-4799-2, $89.95 / £57.99 Also available as an eBook

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Probabilistic Models for Dynamical Systems Haym Benaroya, Seon Mi Han, and Mark Nagurka

Selected Contents: Applications. Events and Probability. Random Variable Models. Functions of Random Variables.

Random Processes. Single Degree-of-Freedom Vibration. Multi Degree-of-Freedom Vibration.

Continuous System Vibration. Reliability. Nonlinear and Stochastic Dynamic Models. Nonstationary Models. Monte Carlo Methods. Fluid-Induced Vibration. Probabilistic Models in Controls and Mechatronic Systems. Index.

Solutions manual and figure slides available upon qualifying course adoption

Catalog no. K12264, May 2013, 764 pp.

ISBN: 978-1-4398-4989-7, $119.95 / £76.99 Also available as an eBook

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

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

—Uday Kumar, Luleå University of Technology

“… this book takes a fresh approach to teaching undergraduate engineering students the fundamentals of probability. The book exploits students’ existing intuition regarding probabilistic concepts when presenting these concepts in a more rigorous manner. 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 available upon qualifying course adoption

Catalog no. K14453, May 2012, 184 pp.

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

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Statistics for Finance New!

Stochastic Finance An Introduction with Market Examples

Stochastic Processes with Applications to Finance Second Edition

Nicolas Privault

Masaaki Kijima

This comprehensive text presents an introduction to pricing and hedging in financial models, with an emphasis on analytical and probabilistic methods. It demonstrates both the power and limitations of mathematical models in finance. The book starts with the basics of finance and stochastic calculus and builds up to special topics, such as options, derivatives, and credit default and jump processes. Many real examples illustrate the topics and classroom-tested exercises are included in each chapter, with selected solutions at the back of the book.

Tokyo Metropolitan University, Japan

• Provides an introduction to probabilistic methods for studying financial models

• Uses discrete processes to clearly explain difficult concepts in stochastic calculus

• Covers from the basics through to special financial topics

• Describes the dynamics of credit ratings using Markov chains

• Balances mathematical details and practical applications

• Shows students how Monte Carlo simulation is used in financial engineering

• Reviews the necessary probabilistic background

• Addresses recent applications in the pricing of discount bonds and credit derivatives

• Includes many exercises and selected solutions Solutions manual available upon qualifying course adoption

Selected Contents: Assets, Portfolios, and Arbitrage, Discrete-Time Model. Pricing and Hedging in Discrete Time.

Brownian Motion and Stochastic Calculus. The Black-Scholes PDE. Martingale Approach to Pricing and Hedging. Estimation of Volatility. Exotic Options.

American Options. Change of Numéraire and Forward Measures. Forward Rate Modeling. Pricing of Interest Rate Derivatives. Credit Default. Stochastic Calculus for Jump Processes. Pricing and Hedging in Jump Models. Basic Numerical Methods. Appendix.

Exercise Solutions. References. Index.

Catalog no. K20632, January 2014, c. 441 pp.

ISBN: 978-1-4665-9402-9, $79.95 / £49.99 Also available as an eBook

This accessible text presents the mathematical theory of financial engineering using only basic mathematical tools that are easy to understand even for those with little mathematical expertise. This second edition covers several important developments in the financial industry. Along with many more examples, it includes a new chapter on the change of measures and pricing of insurance products, a new section on the use of copulas, and two new chapters that offer more coverage of interest rate derivatives and credit derivatives.

• Contains end-of-chapter exercises and numerous examples throughout

Selected Contents: Elementary Calculus: Towards Ito’s Formula.

Elements in Probability. Useful Distributions in Finance. Derivative Securities. Change of Measures and the Pricing of Insurance Products. A DiscreteTime Model for Securities Market. Random Walks.

The Binomial Model. A Discrete-Time Model for Defaultable Securities. Markov Chains. Monte Carlo Simulation. From Discrete to Continuous: Towards the Black-Scholes. Basic Stochastic Processes in Continuous Time. A Continuous-Time Model for Securities Market. Term-Structure Models and Interest-Rate Derivatives. A Continuous-Time Model for Defaultable Securities. References. Index.

Catalog no. K13980, April 2013, 343 pp.

ISBN: 978-1-4398-8482-9, $89.95 / £57.99 Also available as an eBook

For more information and complete contents, visit www.crctextbooks.com

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Statistics for Finance A Course on Statistics for Finance Stanley L. Sclove University of Illinois, Chicago, USA

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.

• 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

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

“… a good review of the mathematics of option pricing.

The chapters are well written and were clear to me.”

—INFORMS Journal on Computing, 2013

Developed from the author’s course on Monte Carlo simulation, 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 handand-paper and MATLAB® coding exercises at the end of every chapter.

• Presents common variance reduction techniques as well as the cross-entropy method • Covers the simulation of diffusion process models

• Presents a careful development of regression, from simple to more complex models

• Assumes minimal background in mathematics and finance

• Integrates regression and time series analysis with applications in finance

• Contains numerous examples of option pricing, risk analysis, and sensitivity analysis

• Requires no prior background in finance

• Includes hand-and-paper and MATLAB coding exercises at the end of each chapter

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

Selected Contents: Review of Probability

Selected Contents:

Brownian Motion

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.

Arbitrage Free Pricing

Catalog no. K14149, December 2012, 269 pp.

ISBN: 978-1-4398-9254-1, $93.95 / £59.99

Sensitivity Analysis

Also available as an eBook

Bibliography

Monte Carlo Simulation Generating Random Variables Variance Reduction Techniques Importance Sampling Stochastic Calculus Simulation of Diffusions Appendices Index Catalog no. K12713, May 2012, 292 pp.

ISBN: 978-1-4398-5824-0, $83.95 / £51.99 Also available as an eBook

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Statistics for Finance An Introduction to Exotic Option Pricing

New!

Peter Buchen

An Object-Oriented Approach in C++

University of Sydney, Australia

“The book presents an entertaining and captivating course in option pricing … . Thanks to the machinery developed by the author and his work group, pricing formulas for even the most complex exotic options are obtained from elementary pricing formulas using elegant arguments and simple algebraic manipulations … a very valuable treatise on exotic option pricing in a Black-Scholes economy. In addition, every chapter concludes with a set of highly relevant and inspiring exercises.”

—Tamás Mátrai, Zentralblatt MATH 1242 Solutions manual available with qualifying course adoption

Catalog no. C9100, February 2012, 296 pp.

ISBN: 978-1-4200-9100-7, $83.95 / £51.99 Also available as an eBook

Computational Methods in Finance Ali Hirsa Caspian Capital Management, LLC, New York, USA

“The depth and breadth of this stand-alone textbook on computational methods in finance is astonishing. It brings together a full-spectrum of methods with many practical examples. … This book provides plenty of exercises and realistic case studies. Those who work through them will gain a deep understanding of the modern computational methods in finance. This uniquely comprehensive and well-written book will undoubtedly prove invaluable to many researchers and practitioners. In addition, it seems to be an excellent teaching book.”

—Lasse Koskinen, International Statistical Review, 2013

Catalog no. K11454, September 2012, 444 pp.

ISBN: 978-1-4398-2957-8, $93.95 / £62.99 Also available as an eBook

Quantitative Finance Erik Schlogl University of Technology, Sydney, Australia

“The three core competencies of a successful quant: firm grasp of theory, strong command of numerical methods, and software design and development skills are taught in parallel, inseparable in the book as they are in the real world.

A fantastic resource for students looking to become quants, the book sets a standard on how practically relevant quantitative finance should be taught.”

—Vladimir V. Piterbarg, Barclays

“Students and practitioners of quantitative analysis have long wanted a detailed exposition of computational finance that includes implementation details and quality C++ code. Their desires are no longer unrequited—this book contains a clear and careful discussion of many of the key derivatives pricing models together with object-oriented C++ code.

Substantial discussion of the design choices made is also included. I believe that this book is destined to be part of every financial engineer’s toolkit.”

—Mark Joshi, University of Melbourne

• Takes a practical approach to quantitative finance problems, such as term structure fitting, pricing fixed income instruments, option pricing, and derivative pricing • Starts from simple building blocks, gradually developing more complex and powerful methods and models • Implements financial models using the de facto industry-standard programming language C++, with code, examples, and exercises available on the author’s website

Selected Contents: A Brief Review of the C++ Programming Language.

Basic Building Blocks.

Lattice Models for Option Pricing.

The Black/Scholes World.

Finite Difference Methods.

Implied Volatility and Volatility Smiles.

Monte Carlo Simulation.

The Heath/Jarrow/Morton Model.

Appendices.

References.

Index.

Catalog no. C4797, December 2013, 354 pp.

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

For more information and complete contents, visit www.crctextbooks.com

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Statistics for Biological Sciences

Statistics for Social Science & Psychology New!

New!

Foundational and Applied Statistics for Biologists Using R Ken A. Aho Idaho State University, Pocatello, USA

Full of biological applications, exercises, and interactive graphical examples, this text presents comprehensive coverage of both modern analytical methods and statistical foundations. The author harnesses the inherent properties of the R environment to enable students to examine the code of complicated procedures step by step and thus better understand the process of obtaining analysis results. The graphical capabilities of R are used to provide interactive demonstrations of simple to complex statistical concepts. R code and other materials are available online.

Nonparametric Statistics for Social and Behavioral Sciences M. Kraska-MIller Auburn University, Alabama, USA

Incorporating a hands-on pedagogical approach, this text presents the concepts, principles, and methods used in performing many nonparametric procedures. It also demonstrates practical applications of the most common nonparametric procedures using IBM’s SPSS software. The text is the only current nonparametric book written specifically for students in the behavioral and social sciences. With examples of real-life research problems, it emphasizes sound research designs, appropriate statistical analyses, and accurate interpretations of results.

• Covers a wide range of analytical topics, including bootstrapping, Bayesian MCMC procedures, regression, model selection, GLMs, GAMs, nonlinear models, ANOVA, mixed effects models, and permutation approaches

• Explains common nonparametric statistical procedures for research designs in nontechnical terms

• Emphasizes the understanding of statistical foundations

• Requires no prerequisite courses in mathematics and statistics, presenting statistical formulas for illustration purposes only

• Provides R code for all analyses and uses R to generate the figures • Includes many biological examples throughout and extensive exercises at the end of each chapter

• Includes examples of the procedures in real-life research problems

• Shows the connection between research and data analysis without burdening students with more technicalities than necessary • Illustrates data input methods using SPSS

• Reviews linear algebra applications and additional mathematical reference material in the appendix

• Integrates many charts, graphs, and tables to help students visualize the data and results

• Offers an introduction to R and R code for each chapter on the author’s website

• Contains exercises and references in every chapter

Figure slides available upon qualifying course adoption

Solutions manual and figure slides available upon qualifying course adoption

Selected Contents: FOUNDATIONS: Philosophical and Historical Foundations. Introduction to Probability. Probability Density Functions. Parameters and Statistics. Interval Estimation: Sampling Distributions, Resampling Distributions, and Simulation Distributions.

Hypothesis Testing. Sampling Design and Experimental Design. APPLICATIONS: Correlation.

Regression. ANOVA. Tabular Analyses. Appendix.

References. Index.

Catalog no. K13403, December 2013, 618 pp.

ISBN: 978-1-4398-7338-0, $69.95 / £44.99 Also available as an eBook

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Selected Contents: Introduction to Research in Social and Behavioral Sciences. Introduction to Nonparametric Statistics.

Analysis of Data to Determine Association and Agreement. Analyses for Two Independent Samples.

Analysis of Multiple Independent Samples. Analysis of Two Dependent Samples. Tests for Multiple Related Samples. Analysis of Single Samples. Index.

Catalog no. K14678, December 2013, 260 pp.

ISBN: 978-1-4665-0760-9, $89.95 / £57.99 Also available as an eBook

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Accessible Introductions to Bayesian Analysis for Your Students Bayesian Data Analysis Third Edition Features: • Focuses on the use of Bayesian inference in practice, with many examples of real statistical analyses throughout • Includes plenty of exercises and bibliographic notes at the end of each chapter • Provides data sets, solutions to selected exercises, and other material online Catalog no. K11900, November 2013, 675 pp.

ISBN: 978-1-4398-4095-5, $69.95 / £44.99 Also available as an eBook See p. 6 for more details

The BUGS Book A Practical Introduction to Bayesian Analysis Features: • Covers all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity • Contains a large number of worked examples and applications from a wide range of disciplines • Includes detailed exercises and solutions on a supporting website Catalog no. C8490, October 2012, 399 pp.

Soft Cover, ISBN: 978-1-58488-849-9 $52.95 / £25.99 Also available as an eBook See p. 14 for more details

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