Contents Introductory Statistics & General References ............3 Statistical Theory & Methods ....................................4 Computational Statistics ..........................................10 Biostatistics ................................................................13 Page 3

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Statistical Genetics & Bioinformatics........................15 Statistics for Engineering & Physical Science ..........16 Statistics for Finance..................................................18 Statistics for Biological Sciences................................21 Statistics for Social Science & Psychology................22

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Introductory Statistics & General References Introduction to Probability with Texas Hold’em Examples Frederic Paik Schoenberg University of California-Los Angeles, USA

Introduction to Probability with Texas Hold’em Examples illustrates both standard and advanced probability topics using the popular poker game of Texas Hold’em, rather than the typical balls in urns. The author uses students’ natural interest in poker to teach important concepts in probability. This classroom-tested book covers the main subjects of a standard undergraduate probability course, including basic probability rules, standard models for describing collections of data, and the laws of large numbers. It also discusses several more advanced topics, such as the ballot theorem, the arcsine law, and random walks, as well as some specialized poker issues, such as the quantification of luck and skill in Texas Hold’em. Homework problems are provided at the end of each chapter. The author includes examples of actual hands of Texas Hold’em from the World Series of Poker and other major tournaments and televised games. He also explains how to use R to simulate Texas Hold’em tournaments for student projects. R functions for running the tournaments are freely available from CRAN in a package called holdem.

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, $49.95 Also available as an eBook

Introduction to the Theory of Statistical Inference Hannelore Liero University of Potsdam, Germany

Silvelyn Zwanzig Uppsala University, Sweden Series: Chapman & Hall/CRC Texts in Statistical Science

Based on the authors’ lecture notes, this text presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Unlike related textbooks, it combines the theoretical basis of statistical inference with a useful applied toolbox that includes linear models. Suitable for a second semester undergraduate course on statistical inference, the text offers proofs to support the mathematics and does not require any use of measure theory. It illustrates core concepts using cartoons and provides solutions to all examples and problems.

Catalog no. K12437, July 2011, 284 pp. Soft Cover ISBN: 978-1-4398-5292-7, $69.95 Also available as an eBook

A Whistle-Stop Tour of Statistics Brian E. Everitt Professor Emeritus, Institute of Psychiatry, King's College, London

This book introduces basic probability and statistics through bite-size coverage of key topics. It is designed as a revision aid and study guide for undergraduate students, with descriptions of key concepts from probability and statistics in self-contained sections. The text shows how statistics can be applied in the real world, with lots of interesting examples and plenty of diagrams and graphs to illustrate the concepts more clearly.

Catalog no. K13590, December 2011, 211 pp. Soft Cover ISBN: 978-1-4398-7748-7, $39.95

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Statistical Theory & Methods Stationary Stochastic Processes

Generalized Linear Mixed Models

Theory and Applications Georg Lindgren

Modern Concepts, Methods and Applications

Lund University, Sweden

Walter W. Stroup

Series: Chapman & Hall/CRC Texts in Statistical Science

University of Nebraska, Lincoln, Nebraska

In recent years, applications of advanced stochastic processes have expanded greatly. Intended for students taking a second course in stochastic processes, this textbook presents an overview of theory with applications in engineering and science. This book covers key topics such as ergodicity, crossing problems, and extremes. It also includes lots of examples to illustrate the theory. In addition, the author presents many exercises with solutions to enable use as a course text or for self-study.

Series: Chapman & Hall/CRC Texts in Statistical Science

The text is intended for a second course in stationary processes, and the material has been chosen to give a fairly broad overview of the theory behind widely scattered applications in engineering and science. The reader should have some experience with stochastic processes and has felt an urge to know more about “what it really is” and “why.”

Features: • Introduces the theory and applications of advanced stochastic processes • Includes all basic theory together with recent developments from research in the area • Provides exercises with hints to solutions and some full solutions in an appendix • Presents examples to illustrate the theory and highlight applications • Covers key topics including ergodicity, crossing problems, and extremes

Selected Contents: Some Probability and Process Background. Stochastic Analysis. Spectral Representations. Linear Filters – General Properties. Linear Filters – Special Topics. Classical Ergodic Theory and Mixing. Vector Processes and Random Fields. Level Crossings and Excursions.

This text covers statistical modeling using generalized linear mixed models (GLMMs) as the organizing tool. After an overarching introduction to modeling from a contemporary perspective, the book presents the main theory and methods used for setting up estimation and inference for GLMMs. It also describes the major classes of applications with case studies from biostatistics and epidemiology. SAS is included throughout while R is used when SAS does not work well with the GLMM.

Features: • Provides a comprehensive treatment of linear models, integrating the traditional linear models with generalized and mixed extensions • Presents background theory and methods, along with applications and case studies • Discusses current controversies in the field • Implements methods using SAS and R

Selected Contents: Modeling Basics. Design Matters. Setting the Stage. Estimation. Inference, Part I. Inference, Part II. Treatment and Explanatory Variable Structure. Multilevel Models. Best Linear Unbiased Prediction. Rates and Proportions. Counts. Time-to-Event Data. Multinomial Data. Correlated Errors, Part 1: Repeated Measures. Correlated Errors, Part 2: Spatial Variability. Power, Sample Size, and Planning.

Catalog no. K10775, September 2012, c. 560 pp. ISBN: 978-1-4398-1512-0, $89.95 Also available as an eBook

Catalog no. K15489, October 2012, c. 376 pp. ISBN: 978-1-4665-5779-6, $89.95 Also available as an eBook

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Statistical Theory & Methods Applied Categorical and Count Data Analysis

Linear Algebra and Matrix Analysis for Statistics

Wan Tang, Hua He, and Xin M. Tu

Sudipto Banerjee

University of Rochester, New York, USA

University of Minnesota, Minneapolis, USA

Anindya Roy

Series: Chapman & Hall/CRC Texts in Statistical Science

University of Maryland, Baltimore County, USA

Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals of the methodology without using rigorous mathematical arguments.

Series: Chapman & Hall/CRC Texts in Statistical Science

The text covers classic concepts and popular topics, such as contingency tables, logistic models, and Poisson regression models, along with modern areas that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples and even adapt or extend the codes to fit data from their own studies. Designed for a one-semester course for graduate and senior undergraduate students in biostatistics, this self-contained text is also suitable as a self-learning guide for biomedical and psychosocial researchers.

Selected Contents: Introduction. Contingency Tables. Sets of Contingency Tables. Regression Models for Categorical Response. Regression Models for Count Response. Loglinear Models for Contingency Tables. Analyses of Discrete Survival Time. Longitudinal Data Analysis. Evaluation of Instruments. Analysis of Incomplete Data. References. Index.

Catalog no. K10311, June 2012, 384 pp. ISBN: 978-1-4398-0624-1, $89.95

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

Linear algebra and the study of matrix algorithms have become fundamental to the development of statistical models. Using a vector space approach, this book provides an understanding of the major concepts that underlie linear algebra and matrix analysis. Each chapter introduces a key topic such as infinitedimensional spaces and provides illustrative examples. The author examines recent developments in diverse fields such as spatial statistics, machine learning, data mining and social network analysis. Complete in its coverage and accessible to students without prior knowledge of linear algebra, the text also includes results that are useful for traditional statistical applications.

Catalog no. K10023, December 2012, c. 416 pp. ISBN: 978-1-4200-9538-8, $79.95 Also available as an eBook

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Statistical Theory & Methods Practical Multivariate Analysis Fifth Edition Abdelmonem Afifi University of California, Los Angeles, USA

Susanne May University of Washington, Seattle, USA

Virginia A. Clark Consultant, Sequim, Washington, USA Series: Chapman & Hall/CRC Texts in Statistical Science

This new version of the bestselling Computer-Aided Multivariate Analysis has been appropriately renamed to better characterize the nature of the book. Taking into account novel multivariate analyses as well as new options for many standard methods, Practical Multivariate Analysis, Fifth Edition shows readers how to perform multivariate statistical analyses and understand the results. For each of the techniques presented in this edition, the authors use the most recent software versions available and discuss the most modern ways of performing the analysis.

New to the Fifth Edition: • Chapter on regression of correlated outcomes resulting from clustered or longitudinal samples • Reorganization of the chapter on data analysis preparation to reflect current software packages • Use of R statistical software • Updated and reorganized references and summary tables • Additional end-of-chapter problems and data sets The first part of the book provides examples of studies requiring multivariate analysis techniques; discusses characterizing data for analysis, computer programs, data entry, data management, data cleanup, missing values, and transformations; and presents a rough guide to assist in choosing the appropriate multivariate analysis. The second part examines outliers and diagnostics in simple linear regression and looks at how multiple linear regression is employed in practice and as a foundation for understanding a variety of concepts. The final part deals with the core of multivariate analysis, covering canonical correlation, discriminant, logistic regression, survival, principal components, factor, cluster, and log-linear analyses. While the text focuses on the use of R, S-PLUS, SAS, SPSS, Stata, and STATISTICA, other software packages can also be used since the output of most standard statistical programs is explained. Data sets and code are available for download from the book’s web page and CRC Press Online.

Introduction to Statistical Limit Theory Alan M. Polansky Northern Illinois University, Dekalb, USA Series: Chapman & Hall/CRC Texts in Statistical Science

Helping students develop a good understanding of asymptotic theory, Introduction to Statistical Limit Theory provides a thorough yet accessible treatment of common modes of convergence and their related tools used in statistics. It also discusses how the results can be applied to several common areas in the field. The author explains as much of the background material as possible and offers a comprehensive account of the modes of convergence of random variables, distributions, and moments, establishing a firm foundation for the applications that appear later in the book. The text includes detailed proofs that follow a logical progression of the central inferences of each result. It also presents in-depth explanations of the results and identifies important tools and techniques. Through numerous illustrative examples, the book shows how asymptotic theory offers deep insight into statistical problems, such as confidence intervals, hypothesis tests, and estimation. With an array of exercises and experiments in each chapter, this classroom-tested book gives students the mathematical foundation needed to understand asymptotic theory. It covers the necessary introductory material as well as modern statistical applications, exploring how the underlying mathematical and statistical theories work together. A solutions manual available upon qualified course adoption.

Selected Contents: Sequences of Real Numbers and Functions. Random Variables and Characteristic Functions. Convergence of Random Variables. Convergence of Distributions. Convergence of Moments. Central Limit Theorems. Asymptotic Expansions for Distributions. Asymptotic Expansions for Random Variables. Differentiable Statistical Functionals. Parametric Inference. Nonparametric Inference. Appendices. References.

Catalog no. C6604, January 2011, 645 pp. ISBN: 978-1-4200-7660-8, $89.95 Also available as an eBook

Catalog no. K10864, July 2011, 537 pp. ISBN: 978-1-4398-1680-6, $89.95 Also available as an eBook

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Statistical Theory & Methods Applied Time Series Analysis Wayne A. Woodward and Henry L. Gray Southern Methodist University, Dallas, Texas, USA

Alan C. Elliott University of Texas Southwestern Medical Center at Dallas, USA Statistics: A Series of Textbooks and Monographs

Virtually any random process developing chronologically can be viewed as a time series. In economics, closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis includes examples across a variety of fields, develops theory, and provides software to address time series problems in a broad spectrum of fields. The authors organize the information in such a format that graduate students in applied science, statistics, and economics can satisfactorily navigate their way through the book while maintaining mathematical rigor. One of the unique features of Applied Time Series Analysis is the associated software, GW-WINKS, designed to help students easily generate realizations from models and explore the associated model and data characteristics. The text explores many important new methodologies that have developed in time series, such as ARCH and GARCH processes, time varying frequencies (TVF), wavelets, and more. Other programs (some written in R and some requiring S-plus) are available on an associated website for performing computations related to the material in the final four chapters.

Selected Contents: Stationary Time Series. Linear Filters. ARMA Time Series Models. Other Stationary Time Series Models. Nonstationary Time Series Models. Forecasting. Parameter Estimation. Model Identification. Model Building. Vector-Valued (Multivariate) Time Series. Long-Memory Processes. Wavelets. G-Stationary Processes.

Catalog no. K10965, October 2011, 564 pp. ISBN: 978-1-4398-1837-4, $99.95 Also available as an eBook

Nonparametric Statistical Inference Fifth Edition Jean Dickinson Gibbons University of Alabama (Emerita), Tuscaloosa, USA

Subhabrata Chakraborti University of Alabama, Tuscaloosa, USA

Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametric statistics. The fifth edition carries on this tradition while thoroughly revising at least 50 percent of the material.

New to the Fifth Edition: • Updated and revised contents based on recent journal articles in the literature • A new section on goodness-of-fit tests • A new chapter that offers practical guidance on how to choose among the various nonparametric procedures covered • Additional problems and examples • Improved computer figures This book covers the most commonly used nonparametric procedures, carefully stating the assumptions, developing the theory behind the procedures, and illustrating the techniques using realistic research examples from the social, behavioral, and life sciences. For most procedures, they present the tests of hypotheses, confidence interval estimation, sample size determination, power, and comparisons of other relevant procedures. The text also gives examples of computer applications based on Minitab, SAS, and StatXact and compares these examples with corresponding hand calculations. The appendix includes a collection of tables required for solving the dataoriented problems.

Selected Contents: Introduction and Fundamentals. Order Statistics, Quantiles, and Coverages. Tests of Randomness. Tests of Goodness of Fit. One-Sample and PairedSample Procedures. The General Two-Sample Problem. Linear Rank Statistics and the General TwoSample Problem. Linear Rank Tests for the Location Problem. Linear Rank Tests for the Scale Problem. Tests of the Equality of k Independent Samples. Measures of Association for Bivariate Samples. Measures of Association in Multiple Classifications. Asymptotic Relative Efficiency. Analysis of Count Data. Summary. Appendix of Tables. Answers to Problems. References. Index.

Catalog no. C7619, July 2010, 650 pp. ISBN: 978-1-4200-7761-2, $99.95 Also available as an eBook

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Statistical Theory & Methods Introduction to General and Generalized Linear Models Henrik Madsen and Poul Thyregod Technical University of Denmark, Lyngby Series: Chapman & Hall/CRC Texts in Statistical Science

“This book presents a well-structured introduction to both general linear models and generalized linear models. … I would recommend the book as a suitable text for senior undergraduate or postgraduate students studying statistics or a reference for researchers in areas of statistics and its applications.” —Shuangzhe Liu, International Statistical Review, 2012

“This book is targeted to undergraduates in statistics but can be used by researchers as a reference manual as well. It is well written, easy to read and the discussion of the examples is clear. As a complement there is a collection of slides for an introductory course on general, generalized, and mixed effects models in the homepage cited in the preface of this book. This book has a good set of references … I recommend this book as one of the textbooks to be discussed in a course for model building.” —Clarice G.B. Demétrio, Biometrics, February 2012

Features: • Introduces concepts for mixed effects models that allow greater flexibility in model building and the data structures • Illustrates the power of the methods through many real-world examples, including drug development, pollutant emissions, and transportation safety • Uses R throughout to solve the examples • Offers solutions to the problems, additional exercises, a complete set of data for the examples, and a collection of lecture slides on the book’s website

Selected Contents: Introduction. The Likelihood Principle. General Linear Models. Generalized Linear Models. Mixed Effects Models. Hierarchical Models. Real-Life Inspired Problems. Appendices. Bibliography. Index.

Catalog no. C9155, November 2010, 316 pp. ISBN: 978-1-4200-9155-7, $83.95 Also available as an eBook

Bayesian Ideas and Data Analysis An Introduction for Scientists and Statisticians Ronald Christensen University of New Mexico, Albuquerque, New Mexico, USA

Wesley Johnson University of California, Irvine, USA

Adam Branscum Oregon State University, Corvallis, USA

Timothy E. Hanson University of South Carolina, Columbia, USA Series: Chapman & Hall/CRC Texts in Statistical Science

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book’s website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data.

Selected Contents: Prologue. Fundamental Ideas I. Integration versus Simulation. Fundamental Ideas II. Comparing Populations. Simulations. Basic Concepts of Regression. Binomial Regression. Linear Regression. Correlated Data. Count Data. Time to Event Data. Time to Event Regression. Binary Diagnostic Tests. Nonparametric Models. Appendices. References.

Catalog no. K10199, July 2010, 516 pp. ISBN: 978-1-4398-0354-7, $72.95 Also available as an eBook

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Statistical Theory & Methods Design of Experiments An Introduction Based on Linear Models

Principles of Uncertainty

Max D. Morris Iowa State University, Ames, USA

Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

Series: Chapman & Hall/CRC Texts in Statistical Science

Series: Chapman & Hall/CRC Texts in Statistical Science

“It is truly my pleasure to read this book … after reading this book, I benefited by gaining insights into the modeling aspect of experimental design, and consequentially it helped me appreciate the idea of statistical efficiency behind each design and understand the tools used in data analysis. … an excellent reference book that I would recommend to anyone who is serious about learning the nuts and bolts of experimental design and data analysis techniques.”

A careful, complete, and lovingly written exposition of the subjective Bayesian viewpoint by one of its most eloquent and staunch defenders. Summarizes a lifetime of theory, methods, and application developments for the Bayesian inferential engine. A must-read for anyone looking for a deep understanding of the foundations of Bayesian methods and what they offer modern statistical practice.

—Rong Pan, Journal of Quality Technology, Vol. 43, No. 3, July 2011

Catalog no. C9233, July 2010, 370 pp. ISBN: 978-1-58488-923-6, $89.95 Also available as an eBook

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

Mike West Duke University, Durham, North Carolina, USA Series: Chapman & Hall/CRC Texts in Statistical Science

“… a very modern entry to the field of time-series modelling, with a rich reference list of the current literature, including 85 references from 2008 and later. It is well written and I spotted very few typos. This textbook can undoubtedly work as a reference manual for anyone entering the field or looking for an update. … I am certain there is more than enough material within time series to fill an intense one-semester course.” —International Statistical Review, 2011

Catalog no. C9336, May 2010, 368 pp. ISBN: 978-1-4200-9336-0, $94.95 Also available as an eBook

Joseph B. Kadane

—Bradley P. Carlin, Professor and Head of Division of Biostatistics, University of Minnesota, Minneapolis, USA

Written in an appealing, inviting style, and packed with interesting examples, Principles of Uncertainty introduces the most compelling parts of mathematics, computing, and philosophy as they bear on statistics. Although many books present the computation of a variety of statistics and algorithms while barely skimming the philosophical ramifications of subjective probability, this book takes a different tack. By addressing how to think about uncertainty, this book gives readers the intuition and understanding required to choose a particular method for a particular purpose. The book contains: • Introductory chapters examining each new concept or assumption • Just-in-time mathematics—the presentation of ideas just before they are applied • Summary and exercises at the end of each chapter • Discussion of maximization of expected utility • The basics of Markov Chain Monte Carlo computing techniques

Selected Contents: Probability. Conditional Probability and Bayes Theorem. Discrete Random Variables. Probability Generating Functions. Continuous Random Variables. Transformations. Normal Distribution. Making Decisions. Conjugate Analysis. Hierarchical Structuring of a Model. Markov Chain Monte Carlo. Multiparty Problems. Exploration of Old Ideas. Epilogue: Applications.

Catalog no. K12848, May 2011, 503 pp. ISBN: 978-1-4398-6161-5, $89.95 Also available as an eBook

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Computational Statistics

R for Statistics Pierre-Andre Cornillon, Arnaud Guyader, François Husson, Nicolas Jégou, Julie Josse, Maela Kloareg, Eric Matzner-Lober, and Laurent Rouvière Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples. Organized into two sections, the book focuses first on R software, then on the implementation of traditional statistical methods with R. Focusing on R software, the first section covers: • Basic elements of R software and data processing • Clear, concise visualization of results, using simple and complex graphs • Programming basics: pre-defined and user-created functions The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including: • Regression methods • Analyses of variance and covariance • Classification methods • Exploratory multivariate analysis • Clustering methods • Hypothesis tests After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist. Datasets and all the results described in this book are available on the book’s webpage at http://www.agrocampus-ouest.fr/math/RforStat

Selected Contents: An Overview of R Main Concepts Preparing Data R Graphics Making Programs with R Statistical Methods Introduction to the Statistical Methods A Quick Start with R Hypothesis Test Regression Analysis of Variance and Covariance Classification Exploratory Multivariate Analysis Clustering Appendix

Catalog no. K13834, March 2012, 320 pp., Soft Cover, ISBN: 978-1-4398-8145-3, $59.95 Also available as an eBook

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Computational Statistics

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

Ana Kupresanin Lawrence Livermore National Laboratory (LLNL), California, USA Chapman & Hall/CRC The R Series

With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response, Statistical Computing in C++ and R concentrates on the writing of code rather than the development and study of numerical algorithms per se. The book discusses code development in C++ and R and the use of these symbiotic languages in unison. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone. The text begins with some basics of object-oriented languages, followed by a "boot-camp" on the use of C++ and R. The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shell programming.

Features: • • • •

Includes numerous student exercises ranging from elementary to challenging Integrates both C++ and R for the solution of statistical computing problems Uses C++ code in R and R functions in C++ programs Provides downloadable programs, available from the authors’ website

The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems. The text reveals the basic principles of algorithmic thinking essential to the modern statistician as well as the fundamental skill of communicating with a computer through the use of the computer languages C++ and R. The book lays the foundation for original code development in a research environment.

Selected Contents: Introduction Computer representation of numbers A sketch of C++ Generation of pseudo-random numbers Programming in R Creating classes and methods in R Numerical linear algebra Numerical optimization Abstract data structures Data structures in C++ Parallel computing in C++ and R An introduction to Unix An introduction to R C++ library extensions (TR1) The Matrix and Vector classes The ranGen class References Index

Catalog no. C6650, December 2011, 556 pp., ISBN: 978-1-4200-6650-0, $89.95 Also available as an eBook

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Computational Statistics

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

“MCMC freed Bayes from the shackles of conjugate priors and the curse of dimensionality; BUGS then brought MCMC-Bayes to the masses, yielding an astonishing explosion in the number, quality, and complexity of Bayesian inference over a vast array of application areas, from finance to medicine to data mining. The most anticipated applied Bayesian text of the last 20 years, The BUGS Book is like a wonderful album by an established rock supergroup: the pressure to deliver a high-quality product was enormous, but the authors have created a masterpiece well worth the wait. The book offers the perfect mix of basic probability calculus, Bayes and MCMC basics, an incredibly broad array of useful statistical models, and a BUGS tutorial and user manual complete with all the ‘tricks’ one would expect from the team that invented the language. BUGS is the dominant Bayesian software package of the post-MCMC era, and this book ensures it will remain so for years to come by providing accessible yet comprehensive instruction in its proper use. A must-own for any working applied statistical modeler.” —Bradley P. Carlin, Professor and Head of Division of Biostatistics, University of Minnesota, Minneapolis, USA

Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines. The book introduces regression models, techniques for criticism and comparison, and a wide range of modeling issues before going into the vital area of hierarchical models, one of the most common applications of Bayesian methods. It deals with essentials of modeling without getting bogged down in complexity. The book emphasizes model criticism, model comparison, sensitivity analysis to alternative priors, and thoughtful choice of prior distributions—all those aspects of the “art” of modeling that are easily overlooked in more theoretical expositions. More pragmatic than ideological, the authors systematically work through the large range of “tricks” that reveal the real power of the BUGS software, for example, dealing with missing data, censoring, grouped data, prediction, ranking, parameter constraints, and so on. Many of the examples are biostatistical, but they do not require domain knowledge and are generalisable to a wide range of other application areas. Full code and data for examples, exercises, and some solutions can be found on the book’s website.

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

Selected Contents: Introduction: probability and parameters. Monte Carlo simulations using BUGS. Introduction to Bayesian inference. Introduction to Markov chain Monte Carlo methods. Prior distributions. Regression models. Categorical data. Model checking and comparison. Issues in Modelling. Hierarchical models. Specialised models. Different implementations of BUGS. Appendix: BUGS language syntax. Appendix: Functions in BUGS. Appendix: Distributions in BUGS. Bibliography. Index.

Catalog no. C8490, October 2012, c. 400 pp., Soft Cover, ISBN: 978-1-58488-849-9, $49.95 Also available as an eBook

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Biostatistics

Medical Biostatistics Third Edition Abhaya Indrayan Series: Chapman & Hall/CRC Biostatistics

The third edition of this acclaimed book focuses on the statistical aspects of medicine with a medical perspective, showing how biostatistics is a useful tool to manage some medical uncertainties. This edition includes several new topics, provides expanded coverage of many other topics and includes software illustrations. The author presents step-by-step explanations of statistical methods with the help of numerous real-world examples. Guide charts at the beginning of the book enable quick access to the relevant statistical procedure, and the comprehensive index at the end makes it easier to locate terms of interest.

Catalog no. K13952, July 2012, 1008 pp. ISBN: 978-1-4398-8414-0, $129.95 Also available as an eBook

Multivariate Survival Analysis and Competing Risks Martin Crowder Imperial College, University of London, UK Series: Chapman & Hall/CRC Texts in Statistical Science

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It 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. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/ biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

Features:

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

This text illustrates the application of regression models in medical research. Ideal for newcomers to the field, it presents a basic introduction to the most common regression models, including ordinary, logistic, and Cox regression. The text focuses on the interpretation of results common to all regression models, such as handling categorical covariates, nonlinear effects, and interactions. Mathematics is only used to describe the basics of the models and all applications are illustrated with Stata.

Catalog no. K15111, October 2012, c. 496 pp. ISBN: 978-1-4665-1748-6, $89.95 Also available as an eBook

• Provides a broad overview of multivariate survival analysis, competing risks, and counting processes • Contains many real-world examples to illustrate methodology • Presents a clear style aimed at an audience of graduate students in statistics • Offers a supporting R package for the analyses, with some code in the book

Selected Contents: Univariate Survival Analysis: Survival Data. Survival Distributions. Frailty Models. Parametric Methods. Discrete Time: Non- And Semi-Parametric Methods. Continuous-Time: Non- And Semi-Parametric Methods. Multivariate Survival Analysis: Multivariate Data and Distributions. Frailty and Copulas. Repeated Measure. Wear and Degradation. Competing Risks: Continuous Failure Times And Their Causes. Parametric Likelihood Inference. Latent Failure Times: Probability Distributions. Discrete Failure Times in Competing Risks. Hazard-Based Methods for Continuous Failure Times. Latent Failure Times: Identifiability Crises. Counting Processes in Survival Analysis: Some Basic Concepts. Survival Analysis. Non- And Semi-Parametric Methods.

Catalog no. K13489, April 2012, 417 pp. ISBN: 978-1-4398-7521-6, $99.95 Also available as an eBook

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13

Biostatistics

Biostatistics A Computing Approach

Exercises and Solutions in Biostatistical Theory

University of Pittsburgh, Pennsylvania, USA

Lawrence L. Kupper, Brian H. Neelon, and Sean M. O'Brien

Series: Chapman & Hall/CRC Biostatistics

Series: Chapman & Hall/CRC Texts in Statistical Science

The emergence of high-speed computing has facilitated the development of many exciting statistical and mathematical methods in the last 25 years, broadening the landscape of available tools in statistical investigations of complex data. Biostatistics: A Computing Approach focuses on visualization and computational approaches associated with both modern and classical techniques. Furthermore, it promotes computing as a tool for performing both analyses and simulations that can facilitate such understanding.

“… it should appeal to a broader audience of anyone interested in mastering the concepts of probability and mathematical statistics at the advanced undergraduate and beginning graduate levels … Students and instructors of such courses as well as anyone studying on their own to brush up their knowledge of statistical theory will find the book very useful. … Overall, I like this book very much. The problems are carefully chosen and cover a wide range of real-world applications of biostatistical methods. Instructors and students will find this book to be a good source of supplementary problems for practice. … I have taught courses in mathematical statistics on several prior occasions and wish a book like this was available earlier.”

Stewart J. Anderson

As a practical matter, programs in R and SAS are presented throughout the text. In addition to these programs, appendices describing the basic use of SAS and R are provided. Teaching by example, this book emphasizes the importance of simulation and numerical exploration in a modern-day statistical investigation. A few statistical methods that can be implemented with simple calculations are also worked into the text to build insight about how the methods really work. Suitable for students who have an interest in the application of statistical methods but do not necessarily intend to become statisticians, this book has been developed from Introduction to Biostatistics II, which the author taught for more than a decade at the University of Pittsburgh.

Selected Contents: Preface 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

Catalog no. C8342, December 2011, 326 pp. ISBN: 978-1-58488-834-5, $79.95 Also available as an eBook

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—Kaushik Ghosh, Journal of Biopharmaceutical Statistics, Vol. 22, 2012

“… a fairly extensive collection of problems such as might be used in a senior undergraduate or firstyear graduate mathematical statistics course aimed at biostatistics majors. … this book would definitely be of value to students who wanted additional examples and problems related to the material most commonly encountered in a first mathematical statistics course. … I have recommended the book to some of my graduate students who are studying for their qualifying exams. … I would also think that it would be of use to instructors who were interested in identifying examples for use in their lectures, homework, or examinations. …” —Scott Emerson, Biometrics, June 2011

Selected Contents: Basic Probability Theory. Univariate Distribution Theory. Multivariate Distribution Theory. Estimation Theory. Hypothesis Testing Theory. Appendix. References. Index.

Catalog no. C7222, November 2010, 420 pp. Soft Cover ISBN: 978-1-58488-722-5, $51.95 Also available as an eBook

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Statistical Genetics & Bioinformatics Stochastic Modelling for Systems Biology Second Edition Darren J. Wilkinson School of Mathematics and Statistics, Newcastle University, UK Series: Chapman & Hall/CRC Mathematical & Computational Biology

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Re-written to reflect this modern perspective, this second edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context.

Statistics and Data Analysis for Microarrays Using R and Bioconductor Second Edition Sorin Drăghici Wayne State University, Detroit, Michigan, USA Series: Chapman & Hall/CRC Mathematical & Computational Biology

Keeping with the spirit of the first edition, all of the theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership.

Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems.

New in the Second Edition:

New to the Second Edition:

• All examples have been updated to Systems Biology Markup Language Level 3 • All code relating to simulation, analysis, and inference for stochastic kinetic models has been re-written and re-structured in a more modular way • An ancillary website provides links, resources, errata, and up-to-date information on installation and use of the associated R package • More background material on the theory of Markov processes and stochastic differential equations, providing more substance for mathematically inclined readers • Discussion of some of the more advanced concepts relating to stochastic kinetic models, such as random time change representations, Kolmogorov equations, Fokker-Planck equations and the linear noise approximation • Simple modelling of "extrinsic" and "intrinsic" noise

Completely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, gene ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying CD-ROM.

An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional mathematical detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.

Catalog no. K10487, December 2011, 1036 pp. ISBN: 978-1-4398-0975-4, $89.95

With all the necessary prerequisites included, this bestselling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.

Also available as an eBook

Catalog no. K11715, November 2011, 363 pp. ISBN: 978-1-4398-3772-6, $89.95 Also available as an eBook

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15

Statistics for Engineering & Physical Science Probability, Statistics, and Reliability for Engineers and Scientists

Statistical and Econometric Methods for Transportation Data Analysis

Third Edition

Second Edition

Bilal M. Ayyub and Richard H. McCuen

Simon P. Washington

University of Maryland, College Park, USA

In a technological society, virtually every engineer and scientist needs to be able to collect, analyze, interpret, and properly use vast arrays of data. This means acquiring a solid foundation in the methods of data analysis and synthesis. Understanding the theoretical aspects is important, but learning to properly apply the theory to real-world problems is essential. Probability, Statistics, and Reliability for Engineers and Scientists, Third Edition introduces the fundamentals of probability, statistics, reliability, and risk methods to engineers and scientists for the purposes of data and uncertainty analysis and modeling in support of decision making. The third edition of this bestselling text presents probability, statistics, reliability, and risk methods with an ideal balance of theory and applications. Clearly written and firmly focused on the practical use of these methods, it places increased emphasis on simulation, particularly as a modeling tool, applying it progressively with projects that continue in each chapter. This provides a measure of continuity and shows the broad use of simulation as a computational tool to inform decision-making processes. This edition also features expanded discussions of the analysis of variance, including single- and two-factor analyses, and a thorough treatment of Monte Carlo simulation. The authors not only clearly establish the limitations, advantages, and disadvantages of each method, but also show that data analysis is a continuum rather than the isolated application of different methods. Like its predecessors, this book continues to serve its purpose well as both a textbook and a reference. Ultimately, readers will find the content of great value in problem solving and decision making, particularly in practical applications.

Catalog no. K10476, April 2011, 663 pp. ISBN: 978-1-4398-0951-8, $119.95 Also available as an eBook

Queensland University of Technology, Brisbane, Australia

Matthew G. Karlaftis National Technical University of Athens, Greece

Fred L. Mannering Purdue University, West Lafayette, Indiana, USA

The complexity, diversity, and random nature of transportation problems necessitates a broad analytical toolbox. Describing tools commonly used in the field, Statistical and Econometric Methods for Transportation Data Analysis, Second Edition provides an understanding of a broad range of analytical tools required to solve transportation problems. It includes a wide breadth of examples and case studies covering applications in various aspects of transportation planning, engineering, safety, and economics. After a solid refresher on statistical fundamentals, the book focuses on continuous dependent variable models and count and discrete dependent variable models.

“The second edition introduces an especially broad set of statistical methods, which are useful not only for transportation modeling but also for modeling in other disciplines. As a lecturer in both transportation and marketing research, I find this book an excellent textbook for advanced undergraduate, Master’s and Ph.D. students, covering topics from simple descriptive statistics to complex Bayesian models. … It is one of the few books that cover an extensive set of statistical methods needed for data analysis in transportation. The book offers a wealth of examples from the transportation field.” —Itzhak Ditzian, The American Statistician, November 2011

Each chapter clearly presents fundamental concepts and principles and includes numerous references for those seeking additional technical details and applications. To reinforce a practical understanding of the modeling techniques, the data sets used in the text are offered on the book’s CRC Press webpage. PowerPoint and Word presentations for each chapter are also available for download.

Catalog no. C285X, December 2010, 544 pp. ISBN: 978-1-4200-8285-2, $99.95 Also available as an eBook

16

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Statistics for Engineering & Physical Science Transportation Statistics and Microsimulation

Applied Reliability

Clifford H. Spiegelman

Paul A. Tobias

Texas A&M University, College Station, USA

Retired, Austin, Texas, USA

Eun Sug Park

Bloom Energy

Texas Transportation Institute, College Station, USA

Laurence R. Rilett University of Nebraska, Lincoln, USA

By discussing statistical concepts in the context of transportation planning and operations, Transportation Statistics and Microsimulation provides the necessary background for making informed transportation-related decisions. It explains the why behind standard methods and uses real-world transportation examples and problems to illustrate key concepts. Classroom-tested at Texas A&M University, the text covers the statistical techniques most frequently employed by transportation and pavement professionals. To familiarize readers with the underlying theory and equations, it contains problems that can be solved using statistical software. The authors encourage the use of SAS’s JMP package, which enables users to interactively explore and visualize data. Students can buy their own copy of JMP at a reduced price via a postcard in the book. Drawing on the authors’ extensive application of statistical techniques in transportation research and teaching, this textbook explicitly defines the underlying assumptions of the techniques and shows how they are used in practice. It presents terms from both a statistical and a transportation perspective, making conversations between transportation professionals and statisticians smoother and more productive.

Selected Contents: Overview: The Role of Statistics in Transportation Engineering. Graphical Methods for Displaying Data. Numerical Summary Measures. Probability and Random Variables. Common Probability Distributions. Sampling Distributions. Inferences: Hypothesis Testing and Interval Estimation. Other Inferential Procedures: ANOVA and DistributionFree Tests. Inferences Concerning Categorical Data. Linear Regression. Regression Models for Count Data. Experimental Design. Cross-Validation, Jackknife, and Bootstrap Methods for Obtaining Standard Errors. Bayesian Approaches to Transportation Data Analysis. Microsimulation. Appendix.

Catalog no. K10032, October 2010, 383 pp. ISBN: 978-1-4398-0023-2, $59.95 Also available as an eBook

Third Edition

David C. Trindade Since the publication of the second edition of Applied Reliability in 1995, the ready availability of inexpensive, powerful statistical software has changed the way statisticians and engineers look at and analyze all kinds of data. Problems in reliability that were once difficult and time consuming even for experts can now be solved with a few well-chosen clicks of a mouse. However, software documentation has had difficulty keeping up with the enhanced functionality added to new releases, especially in specialized areas such as reliability analysis. Using analysis capabilities in spreadsheet software and two well-maintained, supported, and frequently updated, popular software packages—Minitab and SAS JMP—the third edition of Applied Reliability is an easy-to-use guide to basic descriptive statistics, reliability concepts, and the properties of lifetime distributions such as the exponential, Weibull, and lognormal. The material covers reliability data plotting, acceleration models, life test data analysis, systems models, and much more. The third edition includes a chapter on Bayesian reliability analysis and expanded, updated coverage of repairable system modeling.

Catalog no. C4665, August 2011, 600 pp. ISBN: 978-1-58488-466-8, $89.95 Also available as an eBook

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

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.

Catalog no. K14453, May 2012, 184 pp. ISBN: 978-1-4665-0299-4, $129.95 Also available as an eBook

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17

Statistics for Finance Monte Carlo Simulation with Applications to Finance Hui Wang

An Introduction to Exotic Option Pricing

Brown University, Providence, Rhode Island, USA

Peter Buchen

Series: Chapman & Hall/CRC Financial Mathematics

Series: Chapman & Hall/CRC Financial Mathematics

Developed from the author’s course on Monte Carlo simulation at Brown University, Monte Carlo Simulation with Applications to Finance provides a self-contained introduction to Monte Carlo methods in financial engineering. It is suitable for advanced undergraduate and graduate students taking a onesemester course or for practitioners in the financial industry.

In an easy-to-understand, nontechnical yet mathematically elegant manner, An Introduction to Exotic Option Pricing shows how to price exotic options, including complex ones, without performing complicated integrations or formally solving partial differential equations (PDEs). The author incorporates much of his own unpublished work, including ideas and techniques new to the general quantitative finance community.

The author first presents the necessary mathematical tools for simulation, arbitrary free option pricing, and the basic implementation of Monte Carlo schemes. He then describes variance reduction techniques, including control variates, stratification, conditioning, importance sampling, and cross-entropy. The text concludes with stochastic calculus and the simulation of diffusion processes. 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.

Selected Contents: Review of Probability Brownian Motion

University of Sydney, Australia

The first part of the text presents the necessary financial, mathematical, and statistical background, covering both standard and specialized topics. Using no-arbitrage concepts, the Black–Scholes model, and the fundamental theorem of asset pricing, the author develops such specialized methods as the principle of static replication, the Gaussian shift theorem, and the method of images. A key feature is the application of the Gaussian shift theorem and its multivariate extension to price exotic options without needing a single integration. The second part focuses on applications to exotic option pricing, including dual-expiry, multi-asset rainbow, barrier, lookback, and Asian options. Pushing Black–Scholes option pricing to its limits, the author introduces a powerful formula for pricing a class of multi-asset, multiperiod derivatives. He gives full details of the calculations involved in pricing all of the exotic options.

Variance Reduction Techniques

Taking an applied mathematics approach, this book illustrates how to use straightforward techniques to price a wide range of exotic options within the Black–Scholes framework. These methods can even be used as control variates in a Monte Carlo simulation of a stochastic volatility model.

Importance Sampling

Selected Contents:

Stochastic Calculus

Technical Background

Simulation of Diffusions

Applications to Exotic Option Pricing

Sensitivity Analysis

Catalog no. C9100, February 2012, 296 pp. ISBN: 978-1-4200-9100-7, $79.95

Arbitrage Free Pricing Monte Carlo Simulation Generating Random Variables

Appendices

Also available as an eBook

Bibliography Index

Catalog no. K12713, May 2012, 292 pp. ISBN: 978-1-4398-5824-0, $79.95 Also available as an eBook

18

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Statistics for Finance

Computational Methods in Finance Ali Hirsa Caspian Capital Management, LLC, New York, USA Series: Chapman & Hall/CRC Financial Mathematics

This text addresses a variety of numerical methods for pricing derivative contracts, including Fourier techniques, finite differences, numerical simulation, and Monte Carlo simulation methods—one of the first books to cover all of these techniques. After presenting the basics of pricing techniques, it covers key concepts of calibration and parameter estimation. Written by a popular professor at Columbia University and NYU’s Courant Institute, the book is suitable for any graduate course on computational finance in financial engineering and financial mathematics programs as well as for practitioners interested in computational methods in finance. This book is intended for first-/second- year graduate students in financial engineering/mathematics of finance and for practitioners in financial fields. The intention has been to keep the book self-contained and stand alone. Even though the aim was not to write a book on stochastic calculus or derivatives pricing, in some cases, the author gives enough heuristic explanation that one could move forward without any need to stop reading.

Features: • Covers all the key computational methods in finance • Includes chapters on calibration and parameter estimation • Employs algorithms that can be easily coded • Provides case studies and exercises, with hints/solutions to some exercises at the back of the book

Option Valuation A First Course in Financial Mathematics Hugo D. Junghenn The George Washington University, Washington, D.C., USA Series: Chapman & Hall/CRC Financial Mathematics

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

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

Selected Contents:

Selected Contents:

Pricing Derivatives via Fourier Techniques

Parameter Estimation

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

Catalog no. K11454, August 2012, 436 pp. ISBN: 978-1-4398-2957-8, $89.95

Catalog no. K14090, November 2011, 266 pp. ISBN: 978-1-4398-8911-4, $59.95

Also available as an eBook

Also available as an eBook

Introduction to Finite Differences Derivative Pricing via Numerical Solutions of PDEs/PIDEs Monte Carlo Simulation Calibration

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

19

Statistics for Finance Stochastic Finance

Quantitative Finance

A Numeraire Approach

An Object-Oriented Approach in C++

Jan Vecer

Erik Schlogl

Columbia University, New York, New York, USA

University of Technology, Sydney, Australia

Series: Chapman & Hall/CRC Financial Mathematics

Series: Chapman & Hall/CRC Financial Mathematics

This classroom-tested text provides a deep understanding of derivative contracts. Unlike much of the existing literature, the book treats price as a number of units of one asset needed for an acquisition of a unit of another asset instead of expressing prices in dollar terms exclusively. This numeraire approach leads to simpler pricing options for complex products, such as barrier, lookback, quanto, and Asian options. With many examples and exercises, the text relies on intuition and basic principles, rather than technical computations.

A textbook for students and a reference guide for professionals, this text builds a foundation in the key methods and models of quantitative finance from the perspective of their implementation in C++. It introduces computational finance in a pragmatic manner, focusing on practical implementation. The author takes an object-oriented approach that starts from simple building blocks for assembling more complex and powerful models. The author expresses models and algorithms of the industry-standard C++ language and includes working C++ source code on a CD-ROM that accompanies the book.

Features: • Focuses on fundamental principles of pricing • Shows how to identify the basic assets of a given contract • Explains how to compute the prices of contingent claims in terms of various reference assets • Presents examples of a model independent formula for European call options; a simple method for pricing barrier options, lookback options, and Asian options; and a formula for options on LIBOR • Provides prerequisite material on probability and stochastic calculus in the appendix • Includes solutions to odd-numbered exercises at the back of the book

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

Selected Contents:

Introduction. Elements of Finance. Binomial Model. Diffusion Models. Interest Rate Contracts. Barrier Options. Lookback Options. American Options. Contracts on Three or More Assets: Quantos, Rainbows and "Friends." Asian Options. Jump Models. Appendix. Solutions to Selected Exercises. References. Index.

A Brief Review of the C++ Programming Language. Basic Building Blocks. Portfolio Optimization and Asset Pricing. Lattice Models. The Black/Scholes World. Finite Difference Methods for Partial Differential Equations. Implied Volatility and Implied Distributions. Monte Carlo Simulation. The Heath/Jarrow/Morton Model. The Lognormal Forward Rate "Market Models." Case Studies of the Object-Oriented Approach.

Catalog no. K10632, January 2011, 342 pp. ISBN: 978-1-4398-1250-1, $69.95

Catalog no. C4797, December 2012, c. 506 pp. ISBN: 978-1-58488-479-8, $79.95

Selected Contents:

Also available as an eBook

20

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Statistics for Biological Sciences Introduction to Statistical Data Analysis for the Life Sciences Claus Thorn Ekstrøm University of Copenhagen, Frederiksberg, Denmark

Helle Sørensen

Modelling and Quantitative Methods in Fisheries Second Edition Malcolm Haddon

University of Copenhagen, Frederiksberg, Denmark

CSIRO, Hobart, Tasmania, Australia

Any practical introduction to statistics in the life sciences requires a focus on applications and computational statistics combined with a reasonable level of mathematical rigor. It must offer the right combination of data examples, statistical theory, and computing required for analysis today. It should also involve R, the lingua franca of statistical computing.

With numerous real-world examples, Modelling and Quantitative Methods in Fisheries, Second Edition provides an introduction to the analytical methods used by fisheries’ scientists and ecologists. By following the examples using Excel, readers see the nuts and bolts of how the methods work and better understand the underlying principles. Excel workbooks are available for download from the CRC Press website.

Introduction to Statistical Data Analysis for the Life Sciences covers all the usual material but goes further than other texts to emphasize: • Both data analysis and the mathematics underlying classical statistical analysis • Modeling aspects of statistical analysis with added focus on biological interpretations • Applications of statistical software in analyzing real-world problems and data sets Developed from their courses at the University of Copenhagen, the authors imbue readers with the ability to model and analyze data early in the text and then gradually fill in the blanks with needed probability and statistics theory. While the main text can be used with any statistical software, the authors encourage a reliance on R. They provide a short tutorial for those new to the software and include R commands and output at the end of each chapter. Data sets used in the book are available on a supporting website. Each chapter contains a number of exercises, half of which can be done by hand. The text also contains ten case exercises where readers are encouraged to apply their knowledge to larger data sets and learn more about approaches specific to the life sciences. Ultimately, readers come away with a computational toolbox that enables them to perform actual analysis for real data sets as well as the confidence and skills to undertake more sophisticated analyses as their careers progress.

Catalog no. K11221, August 2010, 427 pp. Soft Cover ISBN: 978-1-4398-2555-6, $69.95 Also available as an eBook

In this second edition, the author has revised all chapters and improved a number of the examples. This edition also includes two entirely new chapters: • Characterization of Uncertainty covers asymptotic errors and likelihood profiles and develops a generalized Gibbs sampler to run a Markov chain Monte Carlo analysis that can be used to generate Bayesian posteriors • Sized-Based Models implements a fully functional size-based stock assessment model using abalone as an example This book continues to cover a broad range of topics related to quantitative methods and modelling. It offers a solid foundation in the skills required for the quantitative study of marine populations. Explaining important and relatively complex ideas and methods in a clear manner, the author presents full, step-bystep derivations of equations as much as possible to enable a thorough understanding of the models and methods.

Selected Contents: Fisheries and Modelling. Simple Population Models. Model Parameter Estimation. Computer-Intensive Methods. Randomization Tests. Statistical Bootstrap Methods. Monte Carlo Modelling. Characterization of Uncertainty. Growth of Individuals. Stock Recruitment Relationships. Surplus Production Models. Age-Structured Models. Size-Based Models. Appendix. Bibliography. Index.

Catalog no. C561X, March 2011, 465 pp. ISBN: 978-1-58488-561-0, $79.95 Also available as an eBook

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21

Statistics for Social Science & Psychology

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

“This is an interesting and valuable book … By gathering a mass of results on that topic into a single volume with references, alternative procedures, and supporting software, the author has provided a valuable service to those interested in these issues, which should probably include anyone teaching the techniques covered in this book. … Recommended to those with a solid background in traditional statistical inference who want a highly competent and comprehensive statement of the cases against traditional statistical inference techniques.” —Robert W. Hayden, MAA Reviews, March 2012

In addition to learning how to apply classic statistical methods, students need to understand when these methods perform well, and when and why they can be highly unsatisfactory. Modern Statistics for the Social and Behavioral Sciences illustrates how to use R to apply both standard and modern methods to correct known problems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practical problems with classic methods were missed for so many years, and why modern techniques have practical value. Designed for a two-semester, introductory course for graduate students in the social sciences, this text introduces three major advances in the field: • Early studies seemed to suggest that normality can be assumed with relatively small sample sizes due to the central limit theorem. However, crucial issues were missed. Vastly improved methods are now available for dealing with non-normality. • The impact of outliers and heavy-tailed distributions on power and our ability to obtain an accurate assessment of how groups differ and variables are related is a practical concern when using standard techniques, regardless of how large the sample size might be. Methods for dealing with this insight are described. • The deleterious effects of heteroscedasticity on conventional ANOVA and regression methods are much more serious than once thought. Effective techniques for dealing with heteroscedasticity are described and illustrated. Requiring no prior training in statistics, Modern Statistics for the Social and Behavioral Sciences provides a graduate-level introduction to basic, routinely used statistical techniques relevant to the social and behavioral sciences. It describes and illustrates methods developed during the last half century that deal with known problems associated with classic techniques. Espousing the view that no single method is always best, it imparts a general understanding of the relative merits of various techniques so that methods can be chosen in an informed manner.

Features: • Covers standard methods as well as the most recent advances and insights regarding when classic methods perform well and when and why they are unsatisfactory • Provides many examples, using data from actual studies, which illustrate the potential problems associated with methods routinely taught and used as well as the practical utility of modern techniques. • Covers over 900 R functions • Includes solutions to selected exercises in an appendix

Selected Contents: Introduction. Numerical and Graphical Summaries of Data. Probability and Related Concepts. Sampling Distributions and Confidence Intervals. Hypothesis Testing. Regression and Correlation. Bootstrap Methods. Comparing Two Independent Groups. Comparing Two Dependent Groups. One-Way Anova. Two-Way and Three-Way Designs. Comparing More Than Two Dependent Groups. Multiple Comparisons. Some Multivariate Methods. Robust Regression and Measures Of Association. Basicmethods for Analyzing Categorical Data. Answers to Selected Exercises. Tables. Basic Matrix Algebra. References. Index.

Catalog no. K11557, August 2011, 862 pp., ISBN: 978-1-4398-3456-5, $89.95 Also available as an eBook

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