Statistics

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CONTENTS Introductory Statistics..............................................3 Statistical Theory & Methods..................................5 Computational Statistics ......................................12 Biostatistics & Epidemiology ................................14 Page 5

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Statistics for Engineering & Physical Science ........15 Statistics for Business, Finance, & Economics........17 Statistics for Biological Sciences ............................20 Statistics for Social Science & Psychology ............22

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Page 12

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Teach with the books you trust.™ GTN18 TMC

Introductory Statistics New!

Using R for Introductory Statistics Second Edition John Verzani CUNY/College of Staten Island, New York, USA

Chapman & Hall/CRC The R Series

This bestseller guides students through the basics of R, helping them overcome a steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular while updating data, examples, and changes to R.

New to the Second Edition: • Increased emphasis on more idiomatic R that provides a grounding in the functionality of base R • Discussions on RStudio that help new R users avoid as many pitfalls as possible • Use of knitr package, which makes code easier to read and therefore easier to reason about • Additional information on computer-intensive approaches • Updated examples and data The book has an accompanying package, UsingR, available from CRAN. The package contains the data sets mentioned in the text, answers to selected problems, a few demonstrations, errata, and sample code from the text.

The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The author emphasizes realistic data and examples and relies on visualization techniques to gather insight. He introduces statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes-complex world of statistical computing.

Solutions manual, lecture slides, and figure slides available upon qualifying course adoption

Contents: DATA. What Is Data? Some R Essentials. Accessing Data by Using Indices. Reading in Other Sources of Data. UNIVARIATE DATA. Categorical Data.

Numeric Data. Shape of a Distribution. BIVARIATE DATA. Pairs of Categorical Variables. Comparing Independent Samples. Relationships in Numeric Data. Simple Linear Regression. MULTIVARIATE DATA. Viewing Multivariate Data. R Basics: Data Frames and Lists. Using Model Formula with Multivariate Data. Lattice Graphics. Types of Data in R. DESCRIBING POPULATIONS. Populations.

Families of Distributions. The Central Limit Theorem.

SIMULATION. The Normal Approximation for the Binomial for loops. Simulations Related to the Central Limit Theorem. Defining a Function.

Investigating Distributions. Bootstrap Samples.

Alternates to for loops. CONFIDENCE INTERVALS. Confidence Interval Ideas. Confidence Intervals for a Population Proportion, p. Confidence Intervals for the Population Mean, µ. Other Confidence Intervals.

Confidence Intervals for Differences. Confidence Intervals for the Median. SIGNIFICANCE TESTS. Significance Test for a Population Proportion.

Significance Test for the Mean (t-Tests). Significance Tests and Confidence Intervals. Significance Tests for the Median. Two-Sample Tests of Proportion. TwoSample Tests of Center. GOODNESS OF FIT. The Chi-Squared Goodness-of-Fit Test. The Chi-Squared Test of Independence. Goodness-of-Fit Tests for Continuous Distributions. LINEAR REGRESSION. The Simple Linear Regression Model. Statistical Inference for Simple Linear Regression. Multiple Linear Regression. ANALYSIS OF VARIANCE. OneWay ANOVA. Using lm() for ANOVA. ANCOVA. TwoWay ANOVA. TWO EXTENSIONS OF THE LINEAR MODEL. Logistic Regression. Nonlinear Models.

Appendices. Index Catalog no. K20484, June 2014, 518 pp.

ISBN: 978-1-4665-9073-1, $59.95 / £38.99 Also available as an eBook

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3

Introductory Statistics The R Student Companion Brian Dennis University of Idaho, Moscow, 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.”

—Journal of Statistical Software, February 2013

• Illustrates how to calculate and graph examples in R for college science and mathematics courses • Provides fully developed exercises based around the main precalculus analysis skills needed in the standard college general education courses in science and math • Presents applications drawn from all sciences and social sciences • Includes the most often used features of R on a reference card in the back of the book • Contains R exercises that can be performed cooperatively in groups or alone

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. Appendix A: Installing R. Appendix B: Getting Help. Appendix C: Common R Expressions. Index.

Catalog no. K13498, September 2012, 360 pp.

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

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

Unlike most books on multivariate methods, this one makes straightforward analyses easy to perform for students who are unfamiliar with advanced mathematical formulae. It 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.

Selected Contents: Frequently Asked Questions. Graphical Presentation of Multivariate Data. Multivariate Tests of Significance. Factor Analysis. Cluster Analysis.

Discriminant Analysis. Multidimensional Scaling.

Correspondence Analysis. 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

Introduction to Probability with Texas Hold’em Examples Frederic Paik Schoenberg University of California, Los Angeles, USA

“… as a teacher, this is definitely a book I would recommend as a pleasant introduction to the world of probability theory.”

—CHANCE, June 2013

“… a refreshing new introduction to the subject matter. It is certainly worth considering for your next year’s intake of students."

—International Statistical Review, 2013

“… it is the laser-like focus of the examples and exercises that sets this book apart from other probability textbooks at this level. … The book is incredibly well researched …”

—MAA Reviews, February 2012

Catalog no. K11367, December 2011, 199 pp.

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

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Statistical Theory & Methods Bestseller!

Bayesian Data Analysis Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin Series: Chapman & Hall/CRC Texts in Statistical Science

“The second edition was reviewed in the September 2004 issue of JASA and we now stand ten years later with an even more impressive textbook … truly what Bayesian data analysis should be. … this being a third edition begets the question … what’s new (when compared with the second edition)? Quite a lot … overall this is truly the reference book for a graduate course on Bayesian statistics and not only Bayesian data analysis.”

—Christian Robert (Université Paris Dauphine) on His Blog, March 2014

Now in its third edition, this classic book continues to take an applied approach to analysis using up-to-date Bayesian methods. Along with new and revised software code, this edition includes four new chapters on nonparametric modeling, updates the discussion of cross-validation and predictive information criteria, and improves convergence monitoring and effective sample size calculations for iterative simulation. It also covers weakly informative priors, boundary-avoiding priors, Hamiltonian Monte Carlo, variational Bayes, and expectation propagation.

• Presents an accessible introduction to Bayesian statistics • 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

Selected Contents: Fundamentals of Bayesian Inference. Fundamentals of Bayesian Data Analysis. Advanced Computation.

Regression Models. Nonlinear and Nonparametric Models. Appendices.

Catalog no. K11900, November 2013, 675 pp.

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

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

“By offering many attractive examples of Bayesian networks and by making use of software that allows one to play with the networks, readers will definitely get a feel for what can be done with Bayesian networks. … the power and also uniqueness of the book stem from the fact that it is essentially practice oriented, but with a clear aim of equipping the developer of Bayesian networks with a clear understanding of the underlying theory. Anyone involved in everyday decision making looking for a better foundation of what is now mainly based on intuition will learn something from the book.”

—Journal of Statistical Theory and Practice, March 2014

“… although there have been several excellent books dedicated to Bayesian networks and related methods, these books tend to be aimed at readers who already have a high level of mathematical sophistication … This book is an exciting development because it addresses this problem.”

—From the Foreword by Judea Pearl, UCLA Computer Science Department and 2011 Turing Award Winner

• Focuses on applications and practical model building using AgenaRisk, a powerful commercial software tool • Includes real examples from finance, software and systems, defense, and the law • Introduces the necessary probability and statistics where required

Selected Contents: There Is More to Assessing Risk Than Statistics. The Need for Causal, Explanatory Models in Risk Assessment. Measuring Uncertainty: The Inevitability of Subjectivity. The Basics of Probability. Bayes’ Theorem and Conditional Probability. From Bayes’ Theorem to Bayesian Networks. Defining the Structure of Bayesian Networks. Building and Eliciting Node Probability Tables. Numeric Variables and Continuous. Hypothesis Testing and Confidence Intervals. Modeling Operational Risk. Systems Reliability Modeling. Bayes and the Law. Appendices.

Catalog no. K10450, November 2012, 524 pp.

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

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5

Statistical Theory & Methods Understanding Advanced Statistical Methods

An Introduction to Generalized Linear Models Third Edition

Peter Westfall

Annette J. Dobson

Texas Tech University, Lubbock, USA

University of Queensland, Herston, Australia

Kevin S.S. Henning

Adrian Barnett

Sam Houston State University, Huntsville, Texas, USA

Queensland University of Technology, Kelvin Grove, Australia

“… explanations are fundamentally sound and aimed well at an upper-level undergrad or early graduate student in a statistics-related field. This is a very worthwhile book: a good class text and a practical reference for applied statisticians.”

—Biometrics

Selected Contents: Model Fitting. Exponential Family and Generalized Linear Models. Estimation. Inference. Normal Linear Models. Binary Variables and Logistic Regression.

Nominal and Ordinal Logistic Regression. Poisson Regression and Log-Linear Models. ...

Catalog no. C9500, May 2008, 320 pp., Soft Cover ISBN: 978-1-58488-950-2, $71.95 / £43.99

Series: Chapman & Hall/CRC Texts in Statistical Science

“This book helps to teach students to explore statistics more deeply, avoiding the typical trap of students learning little about the applications of what they are studying and why they are doing it. I think this book will be very useful in the sense that students will be forced to think differently about things, not only about math and statistics, but also about research and the scientific method.”

—Journal of Applied Statistics, 2014 Solutions manual available upon qualifying course adoption

Catalog no. K14873, April 2013, 569 pp.

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

Also available as an eBook

Generalized Linear Mixed Models

Practical Multivariate Analysis

Modern Concepts, Methods and Applications

Fifth Edition

Walter W. Stroup

Abdelmonem Afifi, Susanne May, and Virginia A. Clark

University of Nebraska–Lincoln, USA

Series: Chapman & Hall/CRC Texts in Statistical Science

“This is a very sound text, which teachers of any course on GLMMs should consider adopting.”

“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.”

—International Statistical Review, 2013

Selected Contents: THE BIG PICTURE: Modeling Basics. Design Matters.

Setting the Stage. ESTIMATION AND INFERENCE ESSENTIALS: Estimation. Inference, Part I: Model Effects. Inference, Part II: Covariance Components.

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 ...

—Journal of Biopharmaceutical Statistics, 2012 Solutions manual available for qualifying instructors

Selected Contents: Preparation for Analysis. Applied Regression Analysis.

Multivariate Analysis. Appendix. References. Index.

Catalog no. K10775, September 2012, 555 pp.

ISBN: 978-1-4398-1512-0, $93.95 / £59.99

Catalog no. K10864, July 2011, 537 pp.

ISBN: 978-1-4398-1680-6, $98.95 / £48.99

Also available as an eBook

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 up-to-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. Some of the research ideas introduced can be developed further for specific applications.

Selected Contents: Introduction. Likelihood-Based Approach.

Computation. Imputation. Propensity Scoring Approach. Nonignorable Missing Data. Longitudinal and Clustered Data. Application to Survey Sampling.

Statistical Matching. Bibliography. Index.

Catalog no. K12249, July 2013, 223 pp.

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

Bayesian Ideas and Data Analysis An Introduction for Scientists and Statisticians Ronald Christensen, Wesley Johnson, Adam Branscum, and Timothy E Hanson Series: Chapman & Hall/CRC Texts in Statistical Science

“Firstly, it provides an intermediate-level course in statistics … given to engineers and scientists requiring substantial statistical analysis, as well as material for a course in Bayesian statistics that is typically offered to statistics students. Secondly, it shows how to perform the analyses by using WinBUGS throughout the text. I would use this book as a basis for a course on Bayesian statistics. It is an excellent text for individual study, and students will find it a valuable reference later in their careers.”

—Journal of the Royal Statistical Society: Series A, October 2011

Catalog no. K10199, July 2010, 516 pp.

ISBN: 978-1-4398-0354-7, $75.95 / £51.99 Also available as an eBook

Statistical Theory A Concise Introduction Felix Abramovich Tel Aviv University, Israel

Ya'acov Ritov The Hebrew University of Jerusalem, Israel

Series: Chapman & Hall/CRC Texts in Statistical Science

“As teachers of theoretical statistics, we can use a new approach, which this text offers. … a helpful resource for teachers of mathematical statistics who are looking for an outline of teaching material and useable depth. Their material attains a workable syllabus, which can be easily augmented with the teacher’s preferred emphasis. This volume will make a solid contribution to any theoretical statistics instructor’s collection due to its convenient size, its scope of coverage, judicious use of examples, and clarity of exposition.”

—The American Statistician, May 2014

Catalog no. K12383, April 2013, 240 pp.

ISBN: 978-1-4398-5184-5, $69.95 / £44.99 Also available as an eBook

Richly Parameterized Linear Models Additive, Time Series, and Spatial Models Using Random Effects James S. Hodges University of Minnesota, Minneapolis, USA

Series: Chapman & Hall/CRC Texts in Statistical Science

“This book is a masterpiece, destined to become a classic. … There is not presently a unified theory, like that for linear regression, to explain how, why, and when our numerical routines give results that should be questioned, or at least examined further. Even so, this book does the best job I have seen of explaining what can go wrong and what the state of the art is. … I am excited by the prospect of teaching a course from this book. Its clarity of thought and presentation are exemplary. I recommend it for anyone who fits complicated models.”

—Michael Lavine, University of Massachusetts Amherst

Catalog no. K12996, November 2013, 469 pp.

ISBN: 978-1-4398-6683-2, $89.95 / £57.99 Also available as an eBook

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7

Statistical Theory & Methods Nonparametric Methods in Statistics with SAS Applications

Applied Categorical and Count Data Analysis

Olga Korosteleva

Wan Tang, Hua He, and Xin M. Tu

California State University, Long Beach, USA

This classroom-tested book teaches students how to apply nonparametric techniques to statistical data.

Along with exercises in 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. A solutions manual is available upon qualifying course adoption.

Selected Contents: Hypotheses Testing for Two Samples. Hypotheses Testing for Several Samples. Tests for Categorical Data. Nonparametric Regression. Nonparametric Generalized Additive Regression. Time-to-Event Analysis. Univariate Probability Density Estimation.

Resampling Methods for Interval Estimation.

Catalog no. K18845, August 2013, 195 pp., Soft Cover ISBN: 978-1-4665-8062-6, $69.95 / £44.99 Also available as an eBook

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

This book is designed for a two-quarter or two-semester post-calculus introduction to probability and mathematical statistics for advanced undergraduate students and graduate students in the mathematics, statistics, and other quantitative sciences.

Selected Contents: 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. ...

Catalog no. K15895, January 2014, 622 pp.

ISBN: 978-1-4665-6046-8, $89.95 / £57.99 Also available as an eBook

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University of Rochester, New York, USA

“Exercises, combined with practical data analyses, will certainly facilitate the adoption of the material.”

—International Statistical Review, 2014

“The combination of more advanced and mathematical explanations, newer topics, and sample code from all major software platforms makes this book a valuable addition to the literature on categorical data analysis.”

—Journal of the American Statistical Association, September 2013

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 Also available as an eBook

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.

Instructors can use the material as classroom examples, homework problems, or examination questions. By mastering the theoretical statistical strategies necessary to solve the exercises, students will be prepared to study even higher-level statistical theory. A solutions manual is available upon qualifying course adoption.

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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Statistical Theory & Methods New!

Nonparametric Statistical Inference

Linear Algebra and Matrix Analysis for Statistics

Fifth Edition Jean Dickinson Gibbons and Subhabrata Chakraborti

Sudipto Banerjee University of Minnesota, School of Public Health, Minneapolis, USA

University of Alabama, Tuscaloosa, USA

“… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. …”

—Biometrics, September 2011

“The book is undoubtedly well written and presents a good balance of theory and applications. It is suitable for teaching as well as self-learning. There are exercises in each chapter, which will be helpful in teaching a course. … I would strongly recommend this book to university libraries, teachers, and undergraduate students … .”

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

Catalog no. C7619, July 2010, 650 pp.

ISBN: 978-1-4200-7761-2, $104.95 / £69.99 Also available as an eBook

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. It is extremely well crafted and proceeds up through that theory at a very good pace. 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

Catalog no. K10023, June 2014, 580 pp.

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

Stationary Stochastic Processes for Scientists and Engineers Georg Lindgren, Holger Rootzen, and Maria Sandsten “… 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 … An outstanding text.”

New!

Introduction to Multivariate Analysis Linear and Nonlinear Modeling Sadanori Konishi Chuo University, Tokyo, Japan

Series: Chapman & Hall/CRC Texts in Statistical Science

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.

This text shows students how to use multivariate analysis to extract useful information from multivariate data and understand the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification, discrimination, dimension reduction, and clustering. Many examples and figures throughout facilitate a deep understanding of the multivariate analysis techniques, including how to select the optimal model.

Catalog no. K20279, October 2013, 330 pp.

ISBN: 978-1-4665-8618-5, $79.95 / £49.99

Catalog no. K16322, June 2014, 338 pp.

ISBN: 978-1-4665-6728-3, $89.95 / £57.99

Also available as an eBook

Also available as an eBook

—Clive Anderson, University of Sheffield

Selected Contents:

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

9

Statistical Theory & Methods New!

New!

Linear Models with R

Introduction to Probability

Second Edition

Joseph K. Blitzstein

University of Bath, UK

Harvard University, Cambridge, Massachusetts, USA

Series: Chapman & Hall/CRC Texts in Statistical Science

Stanford University, California, USA

Julian J. Faraway

Like its widely praised, bestselling predecessor, this second edition explains how to use linear models in physical science, engineering, social science, and business applications. The material on interpreting linear models now distinguishes the main applications of prediction and explanation and introduces elementary notions of causality. This edition also covers QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates. It extensively uses R’s ggplot2 graphics package in addition to base graphics.

• Combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling • Offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs • Demonstrates the flexibility of linear models in many examples • Assumes basic knowledge of R and statistics • Emphasizes intuition over rigorous proofs • Presents exercises at the end of each chapter • Includes datasets and R commands

Selected Contents: Introduction. Estimation. Inference. Prediction.

Explanation. Diagnostics. Problems with the Predictors. Problems with the Error. Transformation.

Model Selection. Shrinkage Methods. Insurance Redlining—A Complete Example. Missing Data.

Categorical Predictors. One Factor Models. Models with Several Factors. Experiments with Blocks.

Appendix: About R. Bibliography. Index.

Catalog no. K14039, July 2014, 286 pp.

ISBN: 978-1-4398-8733-2, $89.95 / £57.99 Also available as an eBook

Jessica Hwang

Series: Chapman & Hall/CRC Texts in Statistical Science

Assuming one semester of calculus, this textbook introduces probability to undergraduate students who want to learn statistics. It clearly explains the importance of widely used distributions in statistics, such as normal, binomial, and Poisson, and explores how they are all connected. The book makes the distributions easier to remember, understand, and work with by illustrating natural applications where they arise, including applications of MCMC. R is used to perform statistical calculations.

• Presents definitions, theorems, and proofs through stories that preserve mathematical precision and generality • Focuses on real-world relevance and statistical thinking • Includes interesting modern applications, such as Google PageRank, legal and medical examples, and applications of MCMC to ecology and cryptography • Explains and connects the most important distributions used in statistics • Contains nearly 600 exercises that reinforce students’ understanding of the material instead of requiring repetitive calculations • Supplements key concepts with memorable diagrams • Explains how to run simulations, make visualizations, and perform statistical calculations using R

Selected Contents: Probability and Counting. Conditional Probability.

Random Variables and Their Distributions.

Expectation. Continuous Random Variables.

Moments. Joint Distributions. Transformations.

Conditional Expectation. Inequalities and Limit Theorems. Markov Chains. Markov Chain Monte Carlo. Poisson Processes. Math. R. Table of Distributions. Bibliography. Index.

Catalog no. K16714, August 2014, c. 596 pp.

Pack - Book and Ebook ISBN: 978-1-4665-7557-8, $99.95 / £49.99 Also available as an eBook

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Statistical Theory & Methods New!

New!

Analysis of Categorical Data with R

Statistical Inference

Christopher R. Bilder University of Nebraska–Lincoln, USA

Thomas M. Loughin Simon Fraser University, Surrey, British Columbia, Canada

“This book gives users the full scoop when it comes to analyzing categorical data of all types, and it does so in an easy-to-understand way, giving confidence to the reader to go ahead and apply the ideas in practice. … Through the special attention paid to teaching the basics of R, as well as providing stepby-step particulars in using R in each separate analysis, Bilder and Loughin help establish and promote a group of confident, comfortable users of this software that can seem a mystery to many. I highly and happily recommend this book to anyone who plans to analyze categorical data in their careers— which includes most all of us!”

—Deborah J. Rumsey, The Ohio State University

An Integrated Approach, Second Edition Helio S. Migon, Dani Gamerman, and Francisco Louzada Series: Chapman & Hall/CRC Texts in Statistical Science

This text presents a balanced account of the Bayesian and frequentist approaches to statistical inference.

Along with more examples and exercises, this second edition includes new material on empirical Bayes and penalized likelihoods and their impact on regression models and offers expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models. It also compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two.

Catalog no. K13686, August 2014, c. 385 pp.

ISBN: 978-1-4398-7880-4, $89.95 / £57.99 Also available as an eBook

• Provides descriptions and motivations of the analysis methods as well as worked examples with R code • Highlights applications in a wide range of disciplines, including medicine, psychology, sports, and ecology • Uses R not only as a data analysis method but also as a learning tool • Discusses solutions to problems frequently mishandled in practice, such as how to incorporate diagnostic testing error into an analysis and how to analyze data from a complex survey sampling design • Includes an introduction to R for inexperienced users • Presents an extensive set of exercises at the end of each chapter • Offers data sets, R programs, and videos on the book’s website Solutions manual available upon qualifying course adoption

Selected Contents: Analyzing a Binary Response, Part 1: Introduction.

Analyzing a Binary Response, Part 2: Regression Models. Analyzing a Multicategory Response.

Analyzing a Count Response. Model Selection and Evaluation. Additional Topics. Appendices.

Bibliography. Index.

Catalog no. K12597, August 2014, c. 547 pp.

ISBN: 978-1-4398-5567-6, $89.95 / £49.99

Coming soon!

Nonparametric Statistical Methods Using R John Kloke University of Pittsburgh, Pennsylvania, USA

Joseph McKean Western Michigan University, Kalamazoo, USA

Chapman & Hall/CRC The R Series

Focusing on robust rank-based nonparametric methods, this book covers rank-based fitting and testing for models ranging from simple location models to general linear models for uncorrelated and correlated responses. Illustrated with real data examples using R, each chapter includes a short problem set with data sets. The corresponding example codes are available online. Accessible to nonspecialists, the book also offers an appendix with the technical details of the geometry of rank-based estimation.

Catalog no. K13406, October 2014, c. 288 pp.

ISBN: 978-1-4398-7343-4, $79.95 / £49.99 Also available as an eBook

Also available as an eBook

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11

Computational Statistics A Gentle Introduction to Stata Revised Third Edition Alan C. Acock Oregon State University, Corvallis, USA

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 are used throughout.

• Reflects the features of Stata 11 • Shows how to enter, build, and manage a data set • Supplements basic statistical modeling topics with discussions of effect sizes and standardized coefficients • 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

The BUGS Book A Practical Introduction to Bayesian Analysis David Lunn, Chris Jackson, Nicky Best, Andrew Thomas, and David Spiegelhalter Series: Chapman & Hall/CRC Texts in Statistical Science

“… highly relevant not only for beginners but for advanced users as well. … a notable addition to the growing range of introductory Bayesian textbooks that have been published within the last decade. It is unique in its focus on explicating state-of-the-art computational Bayesian strategies in the WinBUGS software. … The BUGS Book will become a classic Bayesian textbook and provide invaluable guidance to practicing statisticians, academics, and students alike.”

—Journal of Biopharmaceutical Statistics, 2014

Catalog no. C8490, October 2012, 399 pp.

Soft Cover ISBN: 978-1-58488-849-9, $52.95 / £25.99 Also available as an eBook

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

“It could work well as a required text for an advanced undergraduate or graduate course.”

—Computing Reviews, January 2014 Solutions manual 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.

Catalog no. K13525, August 2013, c. 473 pp.

ISBN: 978-1-4398-7590-2, $99.95 / £63.99 Also available as an eBook

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Computational Statistics Statistical Computing with R

Statistical Computing in C++ and R

Maria L. Rizzo

Randall L. Eubank

Bowling Green State University, Ohio, USA

Arizona State University, Tempe, USA

Ana Kupresanin

Chapman & Hall/CRC The R Series

“… an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation.”

—Zentralblatt Math, 2008

This text covers the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. An excellent tutorial on R programming techniques used in practical computational problems, it illustrates every algorithm with at least one fully implemented example coded in R. Key topics covered include the simulation of random variables from probability distributions, the visualization of multivariate data, Monte Carlo integration and variance reduction methods, Monte Carlo methods in inference, bootstrap and jackknife, MCMC methods, and density estimation.

• Provides a tutorial on R programming techniques used in practical computational problems • Covers the most important topics in computational statistics, including Monte Carlo methods, bootstrap, MCMC, and the visualization of multivariate data • Illustrates every algorithm with at least one fully implemented example coded in R • Includes numerous exercises and offers the source code for all examples online Solutions manual available upon qualifying course adoption

Selected Contents: Probability and Statistics Review. Methods for Generating Random Variables. Visualization of Multivariate Data. Monte Carlo Integration and Variance Reduction. Monte Carlo Methods in Inference. Bootstrap and Jackknife. Permutation Tests. Markov Chain Monte Carlo Methods.

Probability Density Estimation. Numerical Methods in R. Appendices. References. Index.

Catalog no. C5459, November 2007, 416 pp.

ISBN: 978-1-58488-545-0, $99.95 / £45.99

Lawrence Livermore National Laboratory, California, USA

“… the first treatment of parallel programming in R that I have seen in a book.

The text is replete with code examples and there are numerous end-of-chapter exercises.”

—International Statistical Review, 2013

Selected Contents: 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.

Catalog no. C6650, December 2011, 556 pp.

ISBN: 978-1-4200-6650-0, $93.95 / £62.99 Also available as an eBook

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

This text emphasizes recurring themes in all statistical algorithms, including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. It touches on topics not usually covered in similar books, namely, systematic verification and the scaling of many established techniques to very large databases. Each chapter includes examples, exercises, and selected solutions.

Selected Contents: Introduction. Computation. Verification. Iteration.

Deduction of Theoretical Properties. Randomization.

Repetition. Scalability and Parallelization.

Bibliography. Index.

Catalog no. K13688, December 2013, 500 pp.

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

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

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Biostatistics & Epidemiology Epidemiology Study Design and Data Analysis, Third Edition Mark Woodward University of Oxford, UK; University of Sydney, Australia; and Johns Hopkins University, Baltimore, Maryland, USA

Regression Models as a Tool in Medical Research Werner Vach

Series: Chapman & Hall/CRC Texts in Statistical Science

Institute of Medical Biometry and Medical Informatics, Freiburg, Germany

Updated and expanded, this popular text 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 and a solutions manual is available upon qualifying course adoption.

“… a very helpful contribution, especially for researchers in medical sciences when performing their statistical analyses and trying to interpret the results obtained. … This book provides plenty of practical knowledge about these basic models and also some of their extensions that is often not easy to find from statistical textbooks or from software manuals. The basic methods are well explained and illustrated by numerous practical examples, mainly using simulated datasets.”

Catalog no. K11828, December 2013, 898 pp.

ISBN: 978-1-4398-3970-6, $99.95 / £49.99

Catalog no. K15111, November 2012, 495 pp.

ISBN: 978-1-4665-1748-6, $93.95 / £59.99

Also available as an eBook

Also available as an eBook

Medical Biostatistics

Multivariate Survival Analysis and Competing Risks

Third Edition Abhaya Indrayan Chapman & Hall/CRC Biostatistics Series

“The third edition of Medical Biostatistics presents an almost complete reference for medical and biostatistics professionals, covering many topics in introductory and intermediate biostatistics. … a good resource for an undergraduate course in biostatistics or related fields. … The strengths of the book are the examples used throughout and the comprehensive coverage in terms of number of topics … a great reference for a researcher in the medical or biostatistics field who is not concerned about mathematical derivations.”

—Journal of the American Statistical Association, March 2014

Catalog no. K13952, August 2012, 1024 pp.

ISBN: 978-1-4398-8414-0, $135.95 / £86.00 Also available as an eBook

—International Statistical Review, 2013

Martin J. Crowder Imperial College, University of London, UK

Series: Chapman & Hall/CRC Texts in Statistical Science

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 Also available as an eBook

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Statistics for Engineering & Physical Science Probability Foundations for Engineers Joel A. Nachlas Virginia Polytechnic Institute and State University, Blacksburg, USA

“… an excellent introductory book on probability for engineers and 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

“… 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

Introduction to Linear Optimization and Extensions with MATLAB® Roy H. Kwon University of Toronto, Ontario, Canada

Operations Research Series

This book fills the need for an introductory book on linear programming that discusses the important ways to mitigate parameter uncertainty. Presenting basics before theory, the author presents a rigorous development of linear programming theory and methods.

The book introduces both stochastic programming and robust optimization as frameworks to deal with parameter uncertainty. This unusual approach— developing these topics in an introductory book— highlights their importance. Since most applications require decisions to be made in the face of uncertainty, the early introduction of these topics facilitates decision making in real-world environments.

• Focuses on state-of-the-art methods for dealing with parameter uncertainty in linear programming

Suitable for a first course in probability theory, this textbook covers theory in an accessible manner and includes numerous practical examples based on engineering applications. The book begins with a summary of set theory and then introduces probability and its axioms. It covers conditional probability, independence, and approximations. An important aspect of the text is the fact that examples are not presented in terms of balls in urns. Many examples do relate to gambling with coins, dice, and cards but most are based on observable physical phenomena familiar to engineering students.

• Illustrates major methods and algorithms using MATLAB

Solutions manual available upon qualifying course adoption

Selected Contents:

Selected Contents:

FUNDAMENTALS: Geometry of Linear Optimization.

Simplex Method. Duality and Sensitivity Analysis.

EXTENSIONS: Decomposition in Linear Optimization. Quadratic Optimization. Interior Point Methods. ROBUST STRATEGIES FOR LINEAR OPTIMIZATION: Stochastic Programming. Robust Linear Optimization.

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: Cumulative Poisson Probabilities. Index.

Catalog no. K14453, May 2012, 184 pp.

ISBN: 978-1-4665-0299-4, $129.95 / £82.00

• Rigorously develops linear programming theory and methods • Includes financial optimization case studies and MATLAB® exercises, with the code available on the book’s CRC Press web page • Contains an extensive bibliography with sources from both classical and recent literature Solutions manual available upon qualifying course adoption

Catalog no. K12905, September 2013, 362 pp.

ISBN: 978-1-4398-6263-6, $99.95 / £63.99 Also available as an eBook

Also available as an eBook

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

15

Statistics for Engineering & Physical Science Introduction to Statistical Process Control Peihua Qiu University of Florida, Gainesville, USA

Series: Chapman & Hall/CRC Texts in Statistical Science

“… an excellent choice as the primary textbook in an SPC course.”

—Changliang Zou, Nankai University

A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily.

Along with covering traditional methods, this book describes many recent SPC methods that improve upon the more established techniques. The author— a leading researcher on SPC—shows how these methods can handle new applications. Pseudo codes are presented for important methods and all R functions and datasets are available on the author’s website.

• Explores the major advantages and limitations of traditional and state-of-the-art SPC methods • Offers practical guidelines on implementing the techniques

Probabilistic Models for Dynamical Systems Second Edition Haym Benaroya, Seon Mi Han, and Mark Nagurka This self-contained book introduces engineering students to randomness in variables, time-dependent functions, and solution methods of the governing equations. After completing the book, students will have a much better understanding of current research and be able to participate in advanced design. A solutions manual is available upon qualifying course adoption.

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.

• Examines the most recent research results in various areas, including univariate and multivariate nonparametric SPC, SPC based on change-point detection, and profile monitoring

Catalog no. K12264, May 2013, 764 pp.

ISBN: 978-1-4398-4989-7, $119.95 / £76.99

• Keeps the mathematical and statistical prerequisites to a minimum, only requiring basic linear algebra, some calculus, and introductory statistics

New!

• Provides more advanced or technical material in discussions at the end of each chapter, along with exercises that encourage students to practice with the methods

With Examples in R

• Presents pseudo codes for important methods • Includes all R functions and datasets on the author’s website

Selected Contents: Introduction. 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

Also available as an eBook

Bayesian Networks Marco Scutari Jean-Baptiste Denis Unité de Recherche Mathématiques et Informatique Appliquées, INRA, Jouy-en-Josas, France

Suitable for graduate students and non-statisticians, this text introduces Bayesian networks using a handson approach with simple yet meaningful examples in R illustrating each step of the modeling process. The book explains the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. It also gives a concise but rigorous treatment of the fundamentals of Bayesian networks, offers an introduction to causal Bayesian networks, and evaluates real-world examples involving causal protein signaling and body composition prediction.

Catalog no. K22427, June 2014, 241 pp.

ISBN: 978-1-4822-2558-7, $89.95 / £57.99 Also available as an eBook

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Statistics for Business, Finance, & Economics Computational Methods in Finance

Quantitative Finance An Object-Oriented Approach in C++

Ali Hirsa Caspian Capital Management, LLC, New York, USA

Erik Schlogl University of Technology, Sydney, Australia

Chapman & Hall/CRC Financial Mathematics Series

“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. … an excellent synthesis of numerical methods needed for solving practical problems in finance. 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. … it seems to be an excellent teaching book.”

—International Statistical Review, 2013

“… there are several sections on topics that are rarely treated in textbooks: saddle point approximations, numerical solution of PIDEs, and others. There is also extensive material on model calibration, including interest rate models and filtering approaches. The book is a very comprehensive and useful reference for anyone, even with limited mathematical background, who wishes to quickly understand techniques from computational finance.”

—Zentralblatt MATH 1260

Helping students accurately price a vast array of derivatives, this self-contained text explains how to solve complex functional equations through numerical methods. Developed from his courses at Columbia University and the Courant Institute of New York University, the author covers key computational methods in finance, model calibration and optimization, and techniques 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, $93.95 / £62.99 Also available as an eBook

Chapman & Hall/CRC Financial Mathematics Series

“I recommend Erik Schlogl’s new book to all those interested in model implementation. From quasi-random sequences to HJM to the Excel interface, with full C++ code, there is something here for everyone.”

—Jim Gatheral, Baruch College, CUNY

“… 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

Catalog no. C4797, November 2013, 354 pp.

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

Stochastic Processes with Applications to Finance Second Edition Masaaki Kijima Tokyo Metropolitan University, Japan

Chapman & Hall/CRC Financial Mathematics Series

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 Business, Finance, & Economics Monte Carlo Simulation with Applications to Finance

Stochastic Finance

Hui Wang

Nicolas Privault

Brown University, Providence, Rhode Island, USA

Nanyang Technological University, Singapore

Chapman & Hall/CRC Financial Mathematics Series

Selected Contents:

“… a good review of the mathematics of option pricing. The chapters are well written and were clear to me.”

—INFORMS Journal on Computing, 2013

“… suitable for the practitioner in search of a hands-on approach to the topic, as well as the student/researcher who wants to have a quick way to know what simulation techniques (in particular for pricing derivatives) are about.”

—Mathematical Reviews, December 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.

Only requiring some familiarity with probability and statistics, the book keeps much of the mathematics at an informal level and avoids technical measure-theoretic jargon to provide a practical understanding of the basics. It includes a large number of examples as well as MATLAB® coding exercises that are designed in a progressive manner so that no prior experience with MATLAB is needed.

An Introduction with Market Examples

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

Brownian Motion and Stochastic Calculus. The BlackScholes 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.

Solutions manual available upon qualifying course adoption

Catalog no. K20632, December 2013, 441 pp.

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

New!

Financial Mathematics A Comprehensive Treatment Giuseppe Campolieti and Roman N. Makarov

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

Wilfrid Laurier University, Waterloo, Ontario, Canada

• Covers the simulation of diffusion process models

“As the owner of literally thousands of books on the mathematics of arbitrage, I’m sorely tempted to sell my collection and buy this book as a replacement. … I commend the authors for their authoritative and comprehensive treatment.”

• Assumes minimal background in mathematics and finance • 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:

—Peter Carr, Morgan Stanley and NYU Courant Master of Science Program in Mathematics in Finance

“The authors treat the subjects rigorously but with plenty of examples, paying close attention to an audience that may encounter the subject matter for the first time, but aware that others will have seen it in different form earlier and may be looking for a different angle. This is a book that will find its way into classrooms worldwide.”

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.

Solutions manual available upon qualifying course adoption

Catalog no. K12713, May 2012, 292 pp.

ISBN: 978-1-4398-5824-0, $83.95 / £51.99

Catalog no. K14142, March 2014, 829 pp.

ISBN: 978-1-4398-9242-8, $89.95 / £57.99

Also available as an eBook

Also available as an eBook

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—Luis Seco, University of Toronto

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Statistics for Business, Finance, & Economics Coming soon!

Coming soon!

Statistics for Finance

Pricing in General Insurance

Erik Lindström, Henrik Madsen, and Jan Nygaard Nielsen Series: Chapman & Hall/CRC Texts in Statistical Science

Bridging the gap between theoretical books on stochastic finance and applied books on financial engineering, this text provides an introduction to statistical methods for finance. Designed for mathematics and statistics students, the book discusses the role that statistics and mathematics play in financial engineering.

It covers the necessary mathematical and statistics background and explores security markets, interest rate models, and term structure. Many data examples illustrate the methods and numerous problems enable the book to be used as a course text or for self-study.

• Presents background material on mathematics and statistics, including probability, linear models, stochastic calculus, and stochastic differential equations • Covers many key topics from financial engineering • Balances theory and applications with numerous examples and problems throughout

Selected Contents: Introduction. Fundamentals. Discrete Time Finance.

Linear Time Series Models. Nonlinear Time Series Models. Kernel Estimators in Time Series Analysis.

Stochastic Calculus. Stochastic Differential Equations.

Continuous Time Security Markets. Stochastic Interest Rate Models. Discrete Time Approximations.

Projections in Hilbert Spaces. Filtering and Prediction Theory. Estimation of Parameters in SDEs. The Term Structure of Interest Rates. Estimation of the Term Structure. Appendices.

Catalog no. K22604, February 2015, c. 320 pp.

ISBN: 978-1-4822-2899-1, $89.95 / £57.99 Also available as an eBook

Pietro Parodi Willis LTD., London, UK

This textbook is a practical introduction to all aspects of general insurance pricing.

Closely following the syllabus of the ST8 exam of the UK Actuarial Profession, the book was developed from the author’s actuarial science course at the Cass Business School. Many real examples and case studies illustrate the various topics while numerous exercises, some based on past ST8 exams, make the book suitable for teaching or selfstudy.

Catalog no. K18873, October 2014, c. 576 pp.

ISBN: 978-1-4665-8144-9, $99.95 / £59.99 Also available as an eBook

Coming soon!

Mathematical Statistics for Applied Econometrics Charles B Moss University of Florida, Gainesville, USA

Unlike standard mathematical statistics texts, this one tailors mathematical statistics topics and real-world examples to economics students. The book gives guidance on computing with Gauss, R, MATLAB®, and Mathematica® and provides numerous exercises. A solutions manual is available upon qualifying course adoption.

Selected Contents: DEFINING RANDOM VARIABLES: Introduction to Statistics, Probability and Econometrics. Random Variables and Probability Distributions. Moments and Moment Generating Functions. Binomial and Normal Random Variables. ESTIMATION: Large Sample Theory. Point Estimation. Interval Estimation.

Testing Hypothesis. ECONOMETRIC APPLICATIONS: Elements of Matrix Analysis. Regression Applications in Econometrics.

Catalog no. K20635, October 2014, c. 376 pp.

ISBN: 978-1-4665-9409-8, $89.95 / £57.99 Also available as an eBook

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

19

Statistics for Biological Sciences Foundational and Applied Statistics for Biologists Using R

Coming soon!

Ken A. Aho

Second Edition

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.

• 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 • Emphasizes the understanding of statistical foundations • 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

Introduction to Statistical Data Analysis for the Life Sciences Claus Thorn Ekstrøm and Helle Sørensen University of Copenhagen, Denmark

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.

Solutions manual available upon qualifying course adoption

Catalog no. K23251, November 2014, c. 504 pp.

Soft Cover ISBN: 978-1-4822-3893-8, $69.95 / £44.99 Also available as an eBook

New!

Statistical Methods in Biology

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

Design and Analysis of Experiments and Regression

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

S.J. Welham, S.A. Gezan, S.J. Clark, and A. Mead

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

Selected Contents Introduction. A Review of Basic Statistics. Principles for Designing Experiments. Models for a Single Factor. Checking Model Assumptions.

Transformations of the Response. Models with Simple Blocking Structure. Extracting Information about Treatments. Models with Complex Blocking Structure. Replication and Power. Dealing with NonOrthogonality. Models for a Single Variate: Simple Linear Regression. Checking Model Fit. Models for Several Variates: Multiple Linear Regression. Models for Variates and Factors. Incorporating Structure: Mixed Models. Models for Curved Relationships.

Models for Non-Normal Responses: Generalized Linear Models. Practical Design and Data Analysis for Real Studies. References. Appendices.

Catalog no. K10432, August 2014, c. 608 pp.

ISBN: 978-1-4398-0878-8, $79.95 / £49.99 Also available as an eBook

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Statistics for Biological Sciences New!

Basic Statistics and Pharmaceutical Statistical Applications Third Edition James E. De Muth University of Wisconsin–Madison, USA

Pharmacy Education Series

Building on its best-selling predecessors, this third edition covers statistical topics most relevant to those in the pharmaceutical industry and pharmacy practice. It focuses on the fundamentals required to understand descriptive and inferential statistics for problem solving. Incorporating new material in virtually every chapter, this third edition now provides information on software applications to assist with evaluating data.

Selected Contents: INTRODUCTION PROBABILITY SAMPLING PRESENTATION MODES MEASURES OF CENTRAL TENDENCY

New to the Third Edition: • Use of Excel® and Minitab® for performing statistical analysis • Discussions of nonprobability sampling procedures, determining if data is normally distributed, evaluation of covariances, and testing for precision equivalence • Expanded sections on regression analysis, chi square tests, tests for trends with ordinal data, and tests related to survival statistics • Additional nonparametric procedures, including the one-sided sign test, Wilcoxon signed-ranks test, and Mood’s median test

THE NORMAL DISTRIBUTION AND DATA TRANSFORMATION CONFIDENCE INTERVALS AND TOLERANCE LIMITS HYPOTHESIS TESTING t-TESTS ONE-WAY ANALYSIS OF VARIANCE (ANOVA) MULTIPLE COMPARISON TESTS FACTORIAL DESIGNS: AN INTRODUCTION CORRELATION REGRESSION ANALYSIS Z-TESTS OF PROPORTIONS

With the help of flow charts and tables, the author dispels some of the anxiety associated with using basic statistical tests in the pharmacy profession and helps students correctly interpret their results using statistical software. Through the text’s problems and worked-out examples, students better understand how the mathematics works, the logic behind many of the equations, and the tests’ outcomes.

CHI SQUARE TESTS MEASURES OF ASSOCIATION ODDS RATIOS AND RELATIVE RISK RATIOS EVIDENCE-BASED PRACTICE: AN INTRODUCTION SURVIVAL STATISTICS NONPARAMETRIC TESTS STATISTICAL TESTS FOR EQUIVALENCE OUTLIER TESTS STATISTICAL ERRORS IN THE LITERATURE APPENDICES INDEX

Catalog no. K20792, April 2014, 847 pp.

ISBN: 978-1-4665-9673-3, $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 Social Science & Psychology Generalized Linear Models for Categorical and Continuous Limited Dependent Variables

Nonparametric Statistics for Social and Behavioral Sciences

Michael Smithson

Incorporating a hands-on pedagogical approach, this text is the only current nonparametric book written specifically for students in the behavioral and social sciences. It demonstrates practical applications of the most common nonparametric procedures using IBM’s SPSS software. A solutions manual is available upon qualifying course adoption.

The Australian National University, Canberra

Edgar C. Merkle University of Missouri, Columbia, USA

Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

Designed for graduate students in the behavioral, social, health, and medical sciences, this text employs generalized linear models, including mixed models, for categorical and limited dependent variables.

Categorical variables include both nominal and ordinal variables. Discrete or continuous limited dependent variables have restricted support, whether through censorship, truncation, or their nature. The book incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages.

• Provides extensive coverage of continuous limited dependent variables, including material on doubly bounded variables • Presents a thorough and consistent treatment of over-dispersion and heteroscedasticity, including tests for them and techniques for modeling them • Integrates coverage of “boundary inflation” issues, such as zero inflation in counts and zero or one inflation in proportions • Highlights extensions of models to include mixed models and Bayesian MCMC estimation • Includes worked examples using the R environment, focusing on packages such as VGAM and betareg

Selected Contents: Introduction and Overview. DISCRETE VARIABLES: Binary. Nominal Multi-Category. Ordinal-Categorical.

Count Data. CONTINUOUS VARIABLES: Doubly Bounded. Censored and Truncated. EXTENSIONS: Multi-Level Models. Bayesian MCMC Estimation.

Appendices. Web-Based Supplementary Materials.

Catalog no. K15187, September 2013, 308 pp.

ISBN: 978-1-4665-5173-2, $89.95 / £57.99 Also available as an eBook

M. Kraska-MIller Auburn University, Alabama, USA

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.

Catalog no. K14678, December 2013, 260 pp.

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

New!

Modern Survey Sampling Arijit Chaudhuri Indian Statistical Institute, Kolkata

Selected Contents: Concepts of Population, Sample, Sampling, Interval and Point Estimation and Posing the Problem of Sampling. Size of Population, Size of Sample, Sampling Design, Sampling Scheme. Unequal Probability Sampling, Ratio-Estimation, Lahiri’s Sampling Scheme, Hartley-Ross Estimator. Stratified and Cluster Sampling. Super-Population Modeling, Prediction Approach, Model-Assisted Approach, and Bayesian Methods. Randomized Response and Indirect Questioning. Small Area Estimation. Network Sampling, Adaptive Sampling, Size Control, and Controlled Sampling. Analytical Surveys.

Bibliography. Index.

Solutions manual available upon qualifying course adoption

Catalog no. K16610, June 2014, 280 pp.

ISBN: 978-1-4665-7260-7, $99.95 / £63.99 Also available as an eBook

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