Statistics

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Contents

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HZL01 MC 7.6.12bh

Introductory Statistics & General References ............3

Statistical Theory & Methods ....................................4

Computational Statistics ..........................................10

Biostatistics ................................................................13

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

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Introduction toProbabilitywith TexasHold’emExamplesFrederic PaikSchoenbergUniversity of California-LosAngeles, USA

Introduction to Probability with Texas Hold’emExamples illustrates both standard and advancedprobability topics using the popular poker game ofTexas Hold’em, rather than the typical balls in urns.The author uses students’ natural interest in poker toteach important concepts in probability.

This classroom-tested book covers the main subjectsof a standard undergraduate probability course,including basic probability rules, standard models fordescribing collections of data, and the laws of largenumbers. It also discusses several more advanced top-ics, such as the ballot theorem, the arcsine law, andrandom walks, as well as some specialized pokerissues, such as the quantification of luck and skill inTexas Hold’em. Homework problems are provided atthe end of each chapter.

The author includes examples of actual hands of TexasHold’em from the World Series of Poker and othermajor tournaments and televised games. He alsoexplains how to use R to simulate Texas Hold’em tour-naments for student projects. R functions for runningthe tournaments are freely available from CRAN in apackage called holdem.

Selected Contents:

Probability Basics. Counting Problems. ConditionalProbability and Independence. Expected Value andVariance. Discrete Random Variables. ContinuousRandom Variables. Collections of Random Variables.Simulation and Approximation Using Computers.Appendices. References and Suggested Reading.Index.

Catalog no. K11367, December 2011, 199 pp.Soft CoverISBN: 978-1-4398-2768-0, $49.95Also available as an eBook

Introduction tothe Theory ofStatisticalInferenceHannelore LieroUniversity of Potsdam, Germany

Silvelyn ZwanzigUppsala University, Sweden

Series: Chapman & Hall/CRCTexts in Statistical Science

Based on the authors’ lecture notes, this text presentsconcise yet complete coverage of statistical inferencetheory, focusing on the fundamental classical princi-ples. Unlike related textbooks, it combines the theo-retical basis of statistical inference with a usefulapplied toolbox that includes linear models. Suitablefor a second semester undergraduate course on sta-tistical inference, the text offers proofs to support themathematics and does not require any use of measuretheory. It illustrates core concepts using cartoons andprovides solutions to all examples and problems.

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

A Whistle-StopTour ofStatisticsBrian E. EverittProfessor Emeritus, Institute ofPsychiatry, King's College,London

This book introduces basic probability and statisticsthrough bite-size coverage of key topics. It is designedas a revision aid and study guide for undergraduatestudents, with descriptions of key concepts fromprobability and statistics in self-contained sections.The text shows how statistics can be applied in thereal world, with lots of interesting examples and plen-ty of diagrams and graphs to illustrate the conceptsmore clearly.

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




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Statistical Theory & Methods

StationaryStochasticProcessesTheory andApplicationsGeorg LindgrenLund University, Sweden


In recent years, applications of advanced stochasticprocesses have expanded greatly. Intended for stu-dents taking a second course in stochastic processes,this textbook presents an overview of theory withapplications in engineering and science. This bookcovers key topics such as ergodicity, crossing prob-lems, and extremes. It also includes lots of examplesto illustrate the theory. In addition, the author pres-ents many exercises with solutions to enable use as acourse text or for self-study.

The text is intended for a second course in stationaryprocesses, and the material has been chosen to give afairly broad overview of the theory behind widelyscattered applications in engineering and science. Thereader should have some experience with stochasticprocesses and has felt an urge to know more about“what it really is” and “why.”

Features:

• Introduces the theory and applications ofadvanced stochastic processes

• Includes all basic theory together with recentdevelopments from research in the area

• Provides exercises with hints to solutions andsome full solutions in an appendix

• Presents examples to illustrate the theory andhighlight applications

• Covers key topics including ergodicity, crossingproblems, and extremes

Selected Contents:

Some Probability and Process Background. StochasticAnalysis. Spectral Representations. Linear Filters –General Properties. Linear Filters – Special Topics.Classical Ergodic Theory and Mixing. VectorProcesses and Random Fields. Level Crossings andExcursions.

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

GeneralizedLinear MixedModelsModern Concepts,Methods andApplicationsWalter W. StroupUniversity of Nebraska, Lincoln,Nebraska

Series: Chapman & Hall/CRC Texts in Statistical Science

This text covers statistical modeling using generalizedlinear mixed models (GLMMs) as the organizing tool.After an overarching introduction to modeling from acontemporary perspective, the book presents themain theory and methods used for setting up estima-tion and inference for GLMMs. It also describes themajor classes of applications with case studies frombiostatistics and epidemiology. SAS is includedthroughout while R is used when SAS does not workwell with the GLMM.

Features:

• Provides a comprehensive treatment of linearmodels, integrating the traditional linear modelswith generalized and mixed extensions

• Presents background theory and methods, alongwith 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: RepeatedMeasures. 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, $89.95Also available as an eBook



5



AppliedCategorical andCount DataAnalysisWan Tang, Hua He, and Xin M. TuUniversity of Rochester, New York, USA


Developed from the authors’ graduate-level biostatis-tics course, Applied Categorical and Count DataAnalysis explains how to perform the statistical analy-sis of discrete data, including categorical and countoutcomes. The authors describe the basic ideas under-lying each concept, model, and approach to givereaders a good grasp of the fundamentals of themethodology without using rigorous mathematicalarguments.

The text covers classic concepts and popular topics,such as contingency tables, logistic models, andPoisson regression models, along with modern areasthat include models for zero-modified count out-comes, parametric and semiparametric longitudinaldata analysis, reliability analysis, and methods for dealing with missing values. R, SAS, SPSS, and Stataprogramming codes are provided for all the exam-ples, enabling readers to immediately experimentwith the data in the examples and even adapt orextend the codes to fit data from their own studies.

Designed for a one-semester course for graduate andsenior undergraduate students in biostatistics, thisself-contained text is also suitable as a self-learningguide for biomedical and psychosocial researchers.

Selected Contents:

Introduction. Contingency Tables. Sets ofContingency Tables. Regression Models forCategorical Response. Regression Models for CountResponse. Loglinear Models for Contingency Tables.Analyses of Discrete Survival Time. Longitudinal DataAnalysis. Evaluation of Instruments. Analysis ofIncomplete Data. References. Index.

Catalog no. K10311, June 2012, 384 pp.ISBN: 978-1-4398-0624-1, $89.95Also available as an eBook

Linear Algebraand MatrixAnalysis forStatisticsSudipto BanerjeeUniversity of Minnesota,Minneapolis, USA

Anindya RoyUniversity of Maryland,Baltimore County, USA


“This beautifully written text is unlike any other instatistical science. It starts at the level of a firstundergraduate course in linear algebra, and takesthe student all the way up to the graduate level,including Hilbert spaces. It is extremely well craftedand proceeds up through that theory at a verygood pace. The statistics chapters are added atjust the right places to motivate the reader andillustrate the theory. The book is compactly writtenand mathematically rigorous, yet the style is livelyas well as engaging. This elegant, sophisticatedwork will serve upper level and graduate statisticseducation well. All and all a book I wish I couldhave written.”

—Jim Zidek, University of British Columbia, Vancouver, Canada

Linear algebra and the study of matrix algorithmshave become fundamental to the development of statistical models. Using a vector space approach, thisbook provides an understanding of the major con-cepts that underlie linear algebra and matrix analysis.Each chapter introduces a key topic such as infinite-dimensional spaces and provides illustrative examples.The author examines recent developments in diversefields such as spatial statistics, machine learning, datamining and social network analysis. Complete in itscoverage and accessible to students without priorknowledge of linear algebra, the text also includesresults that are useful for traditional statistical applica-tions.

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





PracticalMultivariateAnalysisFifth EditionAbdelmonem AfifiUniversity of California, Los Angeles, USA

Susanne MayUniversity of Washington,Seattle, USA

Virginia A. ClarkConsultant, Sequim, Washington, USA


This new version of the bestselling Computer-AidedMultivariate Analysis has been appropriately renamedto better characterize the nature of the book. Takinginto account novel multivariate analyses as well asnew options for many standard methods, PracticalMultivariate Analysis, Fifth Edition shows readershow to perform multivariate statistical analyses andunderstand the results. For each of the techniquespresented in this edition, the authors use the mostrecent software versions available and discuss themost modern ways of performing the analysis.

New to the Fifth Edition:

• Chapter on regression of correlated outcomesresulting from clustered or longitudinal samples

• Reorganization of the chapter on data analysispreparation 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, computerprograms, data entry, data management, data clean-up, missing values, and transformations; and presentsa rough guide to assist in choosing the appropriatemultivariate analysis. The second part examines out-liers and diagnostics in simple linear regression andlooks at how multiple linear regression is employed inpractice and as a foundation for understanding a vari-ety of concepts. The final part deals with the core ofmultivariate analysis, covering canonical correlation,discriminant, logistic regression, survival, principalcomponents, factor, cluster, and log-linear analyses.

While the text focuses on the use of R, S-PLUS, SAS,SPSS, Stata, and STATISTICA, other software packagescan also be used since the output of most standardstatistical programs is explained. Data sets and codeare available for download from the book’s web pageand CRC Press Online.

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

Introduction toStatistical LimitTheoryAlan M. PolanskyNorthern Illinois University,Dekalb, USA


Helping students develop a good understanding ofasymptotic theory, Introduction to Statistical LimitTheory provides a thorough yet accessible treatmentof common modes of convergence and their relatedtools used in statistics. It also discusses how the resultscan be applied to several common areas in the field.

The author explains as much of the background mate-rial as possible and offers a comprehensive account ofthe modes of convergence of random variables, dis-tributions, and moments, establishing a firm founda-tion for the applications that appear later in the book.The text includes detailed proofs that follow a logicalprogression of the central inferences of each result. Italso presents in-depth explanations of the results andidentifies important tools and techniques. Throughnumerous illustrative examples, the book shows howasymptotic theory offers deep insight into statisticalproblems, such as confidence intervals, hypothesistests, and estimation.

With an array of exercises and experiments in eachchapter, this classroom-tested book gives students themathematical foundation needed to understandasymptotic theory. It covers the necessary introducto-ry 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. RandomVariables and Characteristic Functions. Convergenceof Random Variables. Convergence of Distributions.Convergence of Moments. Central Limit Theorems.Asymptotic Expansions for Distributions. AsymptoticExpansions for Random Variables. DifferentiableStatistical Functionals. Parametric Inference.Nonparametric Inference. Appendices. References.

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



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Applied TimeSeries AnalysisWayne A. Woodwardand Henry L. GraySouthern Methodist University,Dallas, Texas, USA

Alan C. ElliottUniversity of Texas SouthwesternMedical Center at Dallas, USA

Statistics: A Series of Textbooksand Monographs

Virtually any random process developing chronologi-cally can be viewed as a time series. In economics,closing prices of stocks, the cost of money, the joblessrate, and retail sales are just a few examples of many.Developed from course notes and extensively class-room-tested, Applied Time Series Analysis includesexamples across a variety of fields, develops theory,and provides software to address time series problemsin a broad spectrum of fields. The authors organizethe information in such a format that graduate stu-dents in applied science, statistics, and economics cansatisfactorily navigate their way through the bookwhile maintaining mathematical rigor.

One of the unique features of Applied Time SeriesAnalysis is the associated software, GW-WINKS,designed to help students easily generate realizationsfrom models and explore the associated model anddata characteristics. The text explores many impor-tant new methodologies that have developed in timeseries, such as ARCH and GARCH processes, time vary-ing frequencies (TVF), wavelets, and more. Other pro-grams (some written in R and some requiring S-plus)are available on an associated website for performingcomputations related to the material in the final fourchapters.

Selected Contents:

Stationary Time Series. Linear Filters. ARMA TimeSeries Models. Other Stationary Time Series Models.Nonstationary Time Series Models. Forecasting.Parameter Estimation. Model Identification. ModelBuilding. Vector-Valued (Multivariate) Time Series.Long-Memory Processes. Wavelets. G-StationaryProcesses.

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

NonparametricStatisticalInferenceFifth EditionJean Dickinson GibbonsUniversity of Alabama (Emerita),Tuscaloosa, USA

Subhabrata ChakrabortiUniversity of Alabama,Tuscaloosa, USA

Since its first publication in 1971, NonparametricStatistical Inference has been widely regarded as thesource for learning about nonparametric statistics.The fifth edition carries on this tradition while thor-oughly revising at least 50 percent of the material.

New to the Fifth Edition:

• Updated and revised contents based on recentjournal 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 nonparamet-ric procedures covered

• Additional problems and examples • Improved computer figures

This book covers the most commonly used nonpara-metric procedures, carefully stating the assumptions,developing the theory behind the procedures, andillustrating the techniques using realistic researchexamples from the social, behavioral, and life sciences.For most procedures, they present the tests ofhypotheses, confidence interval estimation, samplesize determination, power, and comparisons of otherrelevant procedures. The text also gives examples ofcomputer applications based on Minitab, SAS, andStatXact and compares these examples with corre-sponding hand calculations. The appendix includes acollection of tables required for solving the data-oriented problems.

Selected Contents:

Introduction and Fundamentals. Order Statistics,Quantiles, and Coverages. Tests of Randomness.Tests of Goodness of Fit. One-Sample and Paired-Sample Procedures. The General Two-SampleProblem. Linear Rank Statistics and the General Two-Sample Problem. Linear Rank Tests for the LocationProblem. 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 CountData. Summary. Appendix of Tables. Answers toProblems. References. Index.

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





Introduction toGeneral andGeneralizedLinear ModelsHenrik Madsen andPoul ThyregodTechnical University of Denmark,Lyngby


“This book presents a well-structured introductionto both general linear models and generalized lin-ear models. … I would recommend the book as asuitable text for senior undergraduate or post-graduate students studying statistics or a referencefor researchers in areas of statistics and its appli-cations.”

—Shuangzhe Liu, International Statistical Review, 2012

“This book is targeted to undergraduates in statis-tics but can be used by researchers as a referencemanual as well. It is well written, easy to read andthe discussion of the examples is clear. As a com-plement there is a collection of slides for an intro-ductory course on general, generalized, and mixedeffects models in the homepage cited in the prefaceof this book. This book has a good set of references… I recommend this book as one of the textbooksto be discussed in a course for model building.”

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

Features:

• Introduces concepts for mixed effects modelsthat allow greater flexibility in model buildingand the data structures

• Illustrates the power of the methods throughmany real-world examples, including drugdevelopment, 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 onthe book’s website

Selected Contents:

Introduction. The Likelihood Principle. General LinearModels. Generalized Linear Models. Mixed EffectsModels. Hierarchical Models. Real-Life InspiredProblems. Appendices. Bibliography. Index.

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

Bayesian Ideasand DataAnalysisAn Introduction forScientists andStatisticiansRonald ChristensenUniversity of New Mexico,Albuquerque, New Mexico, USA

Wesley JohnsonUniversity of California, Irvine, USA

Adam BranscumOregon State University, Corvallis, USA

Timothy E. HansonUniversity of South Carolina, Columbia, USA


Emphasizing the use of WinBUGS and R to analyze realdata, Bayesian Ideas and Data Analysis: AnIntroduction for Scientists and Statisticians presentsstatistical tools to address scientific questions. It high-lights foundational issues in statistics, the importanceof making accurate predictions, and the need for sci-entists and statisticians to collaborate in analyzingdata. The WinBUGS code provided offers a convenientplatform to model and analyze a wide range of data.

The first five chapters of the book contain core materialthat spans basic Bayesian ideas, calculations, and infer-ence, including modeling one and two sample datafrom traditional sampling models. The text then coversMonte Carlo methods, such as Markov chain MonteCarlo (MCMC) simulation. After discussing linear struc-tures in regression, it presents binomial regression, nor-mal regression, analysis of variance, and Poisson regres-sion, before extending these methods to handle corre-lated data. The authors also examine survival analysisand binary diagnostic testing. A complementary chap-ter on diagnostic testing for continuous outcomes isavailable on the book’s website. The last chapter onnonparametric inference explores density estimationand flexible regression modeling of mean functions.

The appropriate statistical analysis of data involves acollaborative effort between scientists and statisti-cians. Exemplifying this approach, Bayesian Ideasand Data Analysis focuses on the necessary tools andconcepts for modeling and analyzing scientific data.

Selected Contents:

Prologue. Fundamental Ideas I. Integration versusSimulation. Fundamental Ideas II. ComparingPopulations. Simulations. Basic Concepts ofRegression. Binomial Regression. Linear Regression.Correlated Data. Count Data. Time to Event Data.Time to Event Regression. Binary Diagnostic Tests.Nonparametric Models. Appendices. References.




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Design ofExperimentsAn IntroductionBased on LinearModelsMax D. MorrisIowa State University, Ames, USA


“It is truly my pleasure to read this book … afterreading this book, I benefited by gaining insightsinto the modeling aspect of experimental design,and consequentially it helped me appreciate theidea of statistical efficiency behind each design andunderstand the tools used in data analysis. … anexcellent reference book that I would recommendto anyone who is serious about learning the nutsand bolts of experimental design and data analy-sis techniques.”

—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, $89.95Also available as an eBook

Principles ofUncertaintyJoseph B. KadaneCarnegie Mellon University,Pittsburgh, Pennsylvania, USA


A careful, complete, and lovingly written exposi-tion of the subjective Bayesian viewpoint by one ofits most eloquent and staunch defenders.Summarizes a lifetime of theory, methods, andapplication developments for the Bayesian inferen-tial engine. A must-read for anyone looking for adeep understanding of the foundations of Bayesianmethods and what they offer modern statisticalpractice.

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

Written in an appealing, inviting style, and packedwith interesting examples, Principles of Uncertaintyintroduces the most compelling parts of mathematics,computing, and philosophy as they bear on statistics.Although many books present the computation of avariety of statistics and algorithms while barely skim-ming the philosophical ramifications of subjectiveprobability, this book takes a different tack. Byaddressing how to think about uncertainty, this bookgives readers the intuition and understandingrequired to choose a particular method for a particu-lar 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 BayesTheorem. Discrete Random Variables. ProbabilityGenerating Functions. Continuous Random Variables.Transformations. Normal Distribution. MakingDecisions. Conjugate Analysis. HierarchicalStructuring 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, $89.95Also available as an eBook

Time SeriesModeling,Computation, andInferenceRaquel PradoUniversity of California, SantaCruz, USA

Mike WestDuke University, Durham, NorthCarolina, USA


“… a very modern entry to the field of time-seriesmodelling, with a rich reference list of the currentliterature, including 85 references from 2008 andlater. It is well written and I spotted very few typos.This textbook can undoubtedly work as a referencemanual for anyone entering the field or looking foran update. … I am certain there is more thanenough material within time series to fill an intenseone-semester course.”

—International Statistical Review, 2011

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





Computational Statistics

R for StatisticsPierre-Andre Cornillon, Arnaud Guyader, François Husson, Nicolas Jégou, Julie Josse, Maela Kloareg, Eric Matzner-Lober, and Laurent RouvièreAlthough there are currently a wide variety of software packages suitable for the modern statistician, R has the tripleadvantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avecR enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statisticsincludes 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 commentedresults. 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, $59.95Also available as an eBook


11



Statistical Computing in C++ and RRandall L. EubankArizona State University, Tempe, USA

Ana KupresaninLawrence 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 methodologi-cal ideas must be translated into usable code and then numerically evaluated relative to competing procedures. Inresponse, Statistical Computing in C++ and R concentrates on the writing of code rather than the developmentand study of numerical algorithms per se. The book discusses code development in C++ and R and the use of thesesymbiotic 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++ andR. The authors then discuss code development for the solution of specific computational problems that are rele-vant to statistics including optimization, numerical linear algebra, and random number generation. Later chaptersintroduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shellprogramming.

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 thinkingessential to the modern statistician as well as the fundamental skill of communicating with a computer through theuse of the computer languages C++ and R. The book lays the foundation for original code development in aresearch environment.

Selected Contents:

IntroductionComputer representation of numbersA sketch of C++Generation of pseudo-random numbersProgramming in RCreating classes and methods in RNumerical linear algebraNumerical optimizationAbstract data structuresData structures in C++Parallel computing in C++ and RAn introduction to UnixAn introduction to RC++ library extensions (TR1)The Matrix and Vector classesThe ranGen classReferencesIndex

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




The BUGS BookA Practical Introduction to Bayesian AnalysisDavid Lunn, Christoper Jackson, Nicky Best, Andrew Thomas, and David SpiegelhalterUniversity of Cambridge, UK


“MCMC freed Bayes from the shackles of conjugate priors and the curse of dimensionality; BUGS thenbrought MCMC-Bayes to the masses, yielding an astonishing explosion in the number, quality, and com-plexity of Bayesian inference over a vast array of application areas, from finance to medicine to data min-ing. The most anticipated applied Bayesian text of the last 20 years, The BUGS Book is like a wonderfulalbum by an established rock supergroup: the pressure to deliver a high-quality product was enormous, butthe authors have created a masterpiece well worth the wait. The book offers the perfect mix of basic proba-bility calculus, Bayes and MCMC basics, an incredibly broad array of useful statistical models, and a BUGStutorial and user manual complete with all the ‘tricks’ one would expect from the team that invented thelanguage. BUGS is the dominant Bayesian software package of the post-MCMC era, and this book ensuresit will remain so for years to come by providing accessible yet comprehensive instruction in its proper use. Amust-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 theBUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team thatoriginally 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, modelcriticism, and prior sensitivity. It also features a large number of worked examples and a wide range of applicationsfrom various disciplines.

The book introduces regression models, techniques for criticism and comparison, and a wide range of modelingissues before going into the vital area of hierarchical models, one of the most common applications of Bayesianmethods. It deals with essentials of modeling without getting bogged down in complexity. The book emphasizesmodel criticism, model comparison, sensitivity analysis to alternative priors, and thoughtful choice of prior distri-butions—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 revealthe 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 domainknowledge 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 sensi-

tivity • 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 infer-ence. Introduction to Markov chain Monte Carlo methods. Prior distributions. Regression models. Categoricaldata. Model checking and comparison. Issues in Modelling. Hierarchical models. Specialised models. Differentimplementations 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, $49.95Also available as an eBook


13

Biostatistics


MedicalBiostatisticsThird EditionAbhaya Indrayan

Series: Chapman & Hall/CRCBiostatistics

The third edition of this acclaimed book focuses onthe statistical aspects of medicine with a medical per-spective, showing how biostatistics is a useful tool tomanage some medical uncertainties. This editionincludes several new topics, provides expanded coverage of many other topics and includes softwareillustrations. The author presents step-by-step expla-nations of statistical methods with the help of numer-ous real-world examples. Guide charts at the begin-ning of the book enable quick access to the relevantstatistical procedure, and the comprehensive index atthe end makes it easier to locate terms of interest.


MultivariateSurvivalAnalysis andCompetingRisksMartin CrowderImperial College, University ofLondon, UK


Multivariate Survival Analysis and Competing Risksintroduces univariate survival analysis and extends it tothe multivariate case. It covers competing risks andcounting processes and provides many real-worldexamples, exercises, and R code. The text discusses sur-vival data, survival distributions, frailty models, para-metric methods, multivariate data and distributions,copulas, continuous failure, parametric likelihood infer-ence, and non- and semi-parametric methods.

There are many books covering survival analysis, butvery few that cover the multivariate case in any depth.Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises andR code for the examples. The author is renowned forhis clear writing style, and this book continues thattrend. It is an excellent reference for graduate stu-dents and researchers looking for grounding in thisburgeoning field of research.

Features:

• Provides a broad overview of multivariate survival analysis, competing risks, and countingprocesses

• Contains many real-world examples to illustratemethodology

• Presents a clear style aimed at an audience ofgraduate students in statistics

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

Selected Contents:

Univariate Survival Analysis: Survival Data. SurvivalDistributions. Frailty Models. Parametric Methods.Discrete Time: Non- And Semi-Parametric Methods.Continuous-Time: Non- And Semi-ParametricMethods. Multivariate Survival Analysis: MultivariateData and Distributions. Frailty and Copulas.Repeated Measure. Wear and Degradation.Competing Risks: Continuous Failure Times AndTheir Causes. Parametric Likelihood Inference. LatentFailure Times: Probability Distributions. DiscreteFailure Times in Competing Risks. Hazard-BasedMethods for Continuous Failure Times. Latent FailureTimes: Identifiability Crises. Counting Processes inSurvival Analysis: Some Basic Concepts. SurvivalAnalysis. Non- And Semi-Parametric Methods.

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

RegressionModels as aTool in MedicalResearchWerner VachInstitute of Medical Biometry andMedical Informatics, Freiburg,Germany

This text illustrates the application of regression mod-els in medical research. Ideal for newcomers to thefield, it presents a basic introduction to the most com-mon regression models, including ordinary, logistic,and Cox regression. The text focuses on the interpre-tation of results common to all regression models,such as handling categorical covariates, nonlineareffects, and interactions. Mathematics is only used todescribe the basics of the models and all applicationsare illustrated with Stata.

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





Biostatistics

BiostatisticsA ComputingApproachStewart J. AndersonUniversity of Pittsburgh,Pennsylvania, USA

Series: Chapman & Hall/CRCBiostatistics

The emergence of high-speed computing has facili-tated the development of many exciting statisticaland mathematical methods in the last 25 years,broadening the landscape of available tools in statisti-cal investigations of complex data. Biostatistics: AComputing Approach focuses on visualization andcomputational approaches associated with both mod-ern and classical techniques. Furthermore, it promotescomputing as a tool for performing both analyses andsimulations that can facilitate such understanding.

As a practical matter, programs in R and SAS are presented throughout the text. In addition to theseprograms, appendices describing the basic use of SASand R are provided. Teaching by example, this bookemphasizes the importance of simulation and numer-ical exploration in a modern-day statistical investiga-tion. A few statistical methods that can be imple-mented with simple calculations are also worked into the text to build insight about how the methodsreally work.

Suitable for students who have an interest in the appli-cation of statistical methods but do not necessarilyintend to become statisticians, this book has beendeveloped from Introduction to Biostatistics II, whichthe author taught for more than a decade at theUniversity 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, $79.95Also available as an eBook

Exercises andSolutions inBiostatisticalTheoryLawrence L. Kupper,Brian H. Neelon, andSean M. O'Brien


“… it should appeal to a broader audience of any-one interested in mastering the concepts of proba-bility and mathematical statistics at the advancedundergraduate and beginning graduate levels …Students and instructors of such courses as well asanyone studying on their own to brush up theirknowledge of statistical theory will find the bookvery useful. … Overall, I like this book very much.The problems are carefully chosen and cover awide range of real-world applications of biostatis-tical methods. Instructors and students will findthis book to be a good source of supplementaryproblems for practice. … I have taught courses inmathematical statistics on several prior occasionsand wish a book like this was available earlier.”—Kaushik Ghosh, Journal of Biopharmaceutical Statistics,

Vol. 22, 2012

“… a fairly extensive collection of problems such asmight be used in a senior undergraduate or first-year graduate mathematical statistics courseaimed at biostatistics majors. … this book woulddefinitely be of value to students who wanted addi-tional examples and problems related to the mate-rial most commonly encountered in a first mathe-matical statistics course. … I have recommendedthe book to some of my graduate students who arestudying for their qualifying exams. … I would alsothink that it would be of use to instructors whowere interested in identifying examples for use intheir lectures, homework, or examinations. …”

—Scott Emerson, Biometrics, June 2011

Selected Contents:

Basic Probability Theory. Univariate DistributionTheory. Multivariate Distribution Theory. EstimationTheory. Hypothesis Testing Theory. Appendix.References. Index.

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



15

Statistical Genetics & Bioinformatics


StochasticModelling forSystems BiologySecond EditionDarren J. WilkinsonSchool of Mathematics andStatistics, Newcastle University,UK

Series: Chapman & Hall/CRCMathematical & ComputationalBiology

Since the first edition of Stochastic Modelling forSystems Biology, there have been many interestingdevelopments in the use of "likelihood-free" methodsof Bayesian inference for complex stochastic models.Re-written to reflect this modern perspective, this sec-ond edition covers everything necessary for a goodappreciation of stochastic kinetic modelling of biolog-ical networks in the systems biology context.

Keeping with the spirit of the first edition, all of thetheory is presented in a very informal and intuitivemanner, keeping the text as accessible as possible tothe widest possible readership.

New in the Second Edition:

• All examples have been updated to SystemsBiology Markup Language Level 3

• All code relating to simulation, analysis, andinference for stochastic kinetic models has beenre-written and re-structured in a more modularway

• An ancillary website provides links, resources,errata, and up-to-date information on installa-tion and use of the associated R package

• More background material on the theory ofMarkov processes and stochastic differentialequations, providing more substance for mathematically inclined readers

• Discussion of some of the more advanced con-cepts relating to stochastic kinetic models, suchas random time change representations,Kolmogorov equations, Fokker-Planck equationsand the linear noise approximation

• Simple modelling of "extrinsic" and "intrinsic"noise

An effective introduction to the area of stochasticmodelling in computational systems biology, this newedition adds additional mathematical detail and computational methods that will provide a strongerfoundation for the development of more advancedcourses in stochastic biological modelling.

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

Statistics andData Analysisfor MicroarraysUsing R andBioconductorSecond Edition Sorin DrăghiciWayne State University, Detroit, Michigan, USA

Series: Chapman & Hall/CRC Mathematical &Computational Biology

Richly illustrated in color, Statistics and Data Analysisfor Microarrays Using R and Bioconductor, SecondEdition provides a clear and rigorous description ofpowerful analysis techniques and algorithms for min-ing and interpreting biological information. Omittingtedious details, heavy formalisms, and cryptic nota-tions, the text takes a hands-on, example-basedapproach that teaches students the basics of R andmicroarray technology as well as how to choose andapply the proper data analysis tool to specific prob-lems.

New to the Second Edition:

Completely updated and double the size of its prede-cessor, this timely second edition replaces the com-mercial software with the open source R andBioconductor environments. Fourteen new chapterscover such topics as the basic mechanisms of the cell,reliability and reproducibility issues in DNA microar-rays, basic statistics and linear models in R, experi-ment design, multiple comparisons, quality control,data pre-processing and normalization, gene ontol-ogy analysis, pathway analysis, and machine learningtechniques. Methods are illustrated with toy examplesand real data and the R code for all routines is avail-able on an accompanying CD-ROM.

With all the necessary prerequisites included, this best-selling book guides students from very basic notionsto advanced analysis techniques in R andBioconductor. The first half of the text presents anoverview of microarrays and the statistical elementsthat form the building blocks of any data analysis. Thesecond half introduces the techniques most com-monly used in the analysis of microarray data.

Catalog no. K10487, December 2011, 1036 pp.ISBN: 978-1-4398-0975-4, $89.95Also available as an eBook




Statistics for Engineering & Physical Science

Probability,Statistics, andReliability forEngineers andScientistsThird EditionBilal M. Ayyub andRichard H. McCuenUniversity of Maryland, CollegePark, USA

In a technological society, virtually every engineer andscientist needs to be able to collect, analyze, interpret,and properly use vast arrays of data. This meansacquiring a solid foundation in the methods of dataanalysis and synthesis. Understanding the theoreticalaspects is important, but learning to properly applythe theory to real-world problems is essential.

Probability, Statistics, and Reliability for Engineersand Scientists, Third Edition introduces the funda-mentals of probability, statistics, reliability, and riskmethods to engineers and scientists for the purposesof data and uncertainty analysis and modeling in sup-port of decision making.

The third edition of this bestselling text presents prob-ability, statistics, reliability, and risk methods with anideal balance of theory and applications. Clearly writ-ten and firmly focused on the practical use of thesemethods, it places increased emphasis on simulation,particularly as a modeling tool, applying it progres-sively with projects that continue in each chapter. Thisprovides a measure of continuity and shows the broaduse of simulation as a computational tool to informdecision-making processes. This edition also featuresexpanded discussions of the analysis of variance,including single- and two-factor analyses, and a thor-ough treatment of Monte Carlo simulation. Theauthors not only clearly establish the limitations,advantages, and disadvantages of each method, butalso show that data analysis is a continuum ratherthan the isolated application of different methods.

Like its predecessors, this book continues to serve itspurpose well as both a textbook and a reference.Ultimately, readers will find the content of great valuein problem solving and decision making, particularlyin practical applications.

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

Statistical andEconometricMethods forTransportationData AnalysisSecond EditionSimon P. WashingtonQueensland University ofTechnology, Brisbane, Australia

Matthew G. KarlaftisNational Technical University of Athens, Greece

Fred L. ManneringPurdue University, West Lafayette, Indiana, USA

The complexity, diversity, and random nature of trans-portation problems necessitates a broad analyticaltoolbox. Describing tools commonly used in the field, Statistical and Econometric Methods forTransportation Data Analysis, Second Edition pro-vides an understanding of a broad range of analyticaltools required to solve transportation problems. Itincludes a wide breadth of examples and case studiescovering applications in various aspects of transporta-tion planning, engineering, safety, and economics.

After a solid refresher on statistical fundamentals, the book focuses on continuous dependent variablemodels and count and discrete dependent variablemodels.

“The second edition introduces an especially broadset of statistical methods, which are useful not onlyfor transportation modeling but also for modelingin other disciplines. As a lecturer in both trans-portation and marketing research, I find this bookan excellent textbook for advanced undergradu-ate, Master’s and Ph.D. students, covering topicsfrom simple descriptive statistics to complexBayesian models. … It is one of the few books thatcover an extensive set of statistical methods need-ed for data analysis in transportation. The bookoffers a wealth of examples from the transporta-tion field.”

—Itzhak Ditzian, The American Statistician, November 2011

Each chapter clearly presents fundamental conceptsand principles and includes numerous references forthose seeking additional technical details and applica-tions. To reinforce a practical understanding of themodeling techniques, the data sets used in the textare offered on the book’s CRC Press webpage.PowerPoint and Word presentations for each chapterare also available for download.

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



17

Statistics for Engineering & Physical Science


TransportationStatistics andMicrosimulationClifford H. SpiegelmanTexas A&M University, CollegeStation, USA

Eun Sug ParkTexas Transportation Institute,College Station, USA

Laurence R. RilettUniversity of Nebraska, Lincoln, USA

By discussing statistical concepts in the context of transportation planning and operations,Transportation Statistics and Microsimulation pro-vides the necessary background for making informedtransportation-related decisions. It explains the whybehind standard methods and uses real-world trans-portation examples and problems to illustrate keyconcepts.

Classroom-tested at Texas A&M University, the textcovers the statistical techniques most frequentlyemployed by transportation and pavement profes-sionals. To familiarize readers with the underlying the-ory and equations, it contains problems that can besolved using statistical software. The authors encour-age the use of SAS’s JMP package, which enables usersto interactively explore and visualize data. Studentscan buy their own copy of JMP at a reduced price viaa postcard in the book.

Drawing on the authors’ extensive application of sta-tistical techniques in transportation research andteaching, this textbook explicitly defines the underly-ing assumptions of the techniques and shows howthey are used in practice. It presents terms from botha statistical and a transportation perspective, makingconversations between transportation professionalsand statisticians smoother and more productive.

Selected Contents:

Overview: The Role of Statistics in TransportationEngineering. Graphical Methods for Displaying Data.Numerical Summary Measures. Probability andRandom Variables. Common ProbabilityDistributions. Sampling Distributions. Inferences:Hypothesis Testing and Interval Estimation. OtherInferential Procedures: ANOVA and Distribution-Free Tests. Inferences Concerning Categorical Data.Linear Regression. Regression Models for CountData. Experimental Design. Cross-Validation,Jackknife, and Bootstrap Methods for ObtainingStandard Errors. Bayesian Approaches toTransportation Data Analysis. Microsimulation.Appendix.

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

ProbabilityFoundations for EngineersJoel A. NachlasVirginia Polytechnic Institute andState University, Blacksburg, USA

Suitable for a first course inprobability theory, this text-book covers theory in anaccessible manner andincludes numerous practical examples based on engi-neering applications. The book begins with a summa-ry of set theory and then introduces probability and itsaxioms. It covers conditional probability, independ-ence, and approximations. An important aspect of thetext is the fact that examples are not presented interms of "balls in urns." Many examples do relate togambling with coins, dice, and cards, but most arebased on observable physical phenomena familiar toengineering students.


AppliedReliabilityThird EditionPaul A. TobiasRetired, Austin, Texas, USA

David C. TrindadeBloom Energy

Since the publication of thesecond edition of AppliedReliability in 1995, the readyavailability of inexpensive, powerful statistical softwarehas changed the way statisticians and engineers lookat and analyze all kinds of data. Problems in reliabilitythat were once difficult and time consuming even forexperts can now be solved with a few well-chosenclicks of a mouse. However, software documentationhas had difficulty keeping up with the enhanced func-tionality added to new releases, especially in special-ized areas such as reliability analysis.

Using analysis capabilities in spreadsheet software andtwo well-maintained, supported, and frequentlyupdated, popular software packages—Minitab andSAS JMP—the third edition of Applied Reliability is aneasy-to-use guide to basic descriptive statistics, reliability concepts, and the properties of lifetime dis-tributions such as the exponential, Weibull, and log-normal. The material covers reliability data plotting,acceleration models, life test data analysis, systemsmodels, and much more. The third edition includes achapter 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, $89.95Also available as an eBook





Statistics for Finance

Monte CarloSimulation withApplications toFinanceHui WangBrown University, Providence,Rhode Island, USA

Series: Chapman & Hall/CRCFinancial Mathematics

Developed from the author’s course on Monte Carlosimulation at Brown University, Monte CarloSimulation with Applications to Finance provides aself-contained introduction to Monte Carlo methodsin financial engineering. It is suitable for advancedundergraduate and graduate students taking a one-semester course or for practitioners in the financialindustry.

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

Only requiring some familiarity with probability andstatistics, the book keeps much of the mathematics atan informal level and avoids technical measure-theo-retic jargon to provide a practical understanding ofthe basics. It includes a large number of examples aswell as MATLAB® coding exercises that are designed ina progressive manner so that no prior experience withMATLAB is needed.

Selected Contents:

Review of Probability

Brownian Motion

Arbitrage Free Pricing

Monte Carlo Simulation

Generating Random Variables

Variance Reduction Techniques

Importance Sampling

Stochastic Calculus

Simulation of Diffusions

Sensitivity Analysis

Appendices

Bibliography

Index


An Introductionto ExoticOption PricingPeter BuchenUniversity of Sydney, Australia


In an easy-to-understand, nontechnical yet mathe-matically elegant manner, An Introduction to ExoticOption Pricing shows how to price exotic options,including complex ones, without performing compli-cated integrations or formally solving partial differen-tial equations (PDEs). The author incorporates muchof his own unpublished work, including ideas andtechniques new to the general quantitative financecommunity.

The first part of the text presents the necessary financial, mathematical, and statistical background,covering both standard and specialized topics. Usingno-arbitrage concepts, the Black–Scholes model, andthe fundamental theorem of asset pricing, the authordevelops such specialized methods as the principle ofstatic replication, the Gaussian shift theorem, and themethod of images. A key feature is the application ofthe Gaussian shift theorem and its multivariate exten-sion to price exotic options without needing a singleintegration.

The second part focuses on applications to exoticoption pricing, including dual-expiry, multi-asset rain-bow, barrier, lookback, and Asian options. PushingBlack–Scholes option pricing to its limits, the authorintroduces a powerful formula for pricing a class ofmulti-asset, multiperiod derivatives. He gives fulldetails of the calculations involved in pricing all of theexotic options.

Taking an applied mathematics approach, this bookillustrates how to use straightforward techniques toprice a wide range of exotic options within theBlack–Scholes framework. These methods can evenbe used as control variates in a Monte Carlo simula-tion of a stochastic volatility model.

Selected Contents:

Technical Background

Applications to Exotic Option Pricing

Catalog no. C9100, February 2012, 296 pp.ISBN: 978-1-4200-9100-7, $79.95Also available as an eBook



19



ComputationalMethods inFinanceAli HirsaCaspian Capital Management,LLC, New York, USA


This text addresses a variety of numerical methods forpricing derivative contracts, including Fourier tech-niques, finite differences, numerical simulation, andMonte Carlo simulation methods—one of the firstbooks to cover all of these techniques. After present-ing the basics of pricing techniques, it covers key con-cepts of calibration and parameter estimation. Writtenby a popular professor at Columbia University andNYU’s Courant Institute, the book is suitable for anygraduate course on computational finance in financialengineering and financial mathematics programs aswell as for practitioners interested in computationalmethods in finance.

This book is intended for first-/second- year graduatestudents in financial engineering/mathematics offinance and for practitioners in financial fields. Theintention has been to keep the book self-containedand stand alone. Even though the aim was not towrite a book on stochastic calculus or derivatives pric-ing, in some cases, the author gives enough heuristicexplanation that one could move forward withoutany need to stop reading.

Features:

• Covers all the key computational methods infinance

• Includes chapters on calibration and parameterestimation

• Employs algorithms that can be easily coded• Provides case studies and exercises, with

hints/solutions to some exercises at the back ofthe book

Selected Contents:

Pricing Derivatives via Fourier Techniques

Introduction to Finite Differences

Derivative Pricing via Numerical Solutions ofPDEs/PIDEs

Monte Carlo Simulation

Calibration

Parameter Estimation

Catalog no. K11454, August 2012, 436 pp.ISBN: 978-1-4398-2957-8, $89.95Also available as an eBook

OptionValuationA First Course inFinancialMathematicsHugo D. JunghennThe George WashingtonUniversity, Washington, D.C.,USA


Largely self-contained, this classroom-tested text pro-vides a straightforward introduction to the mathe-matics and models used in the valuation of financialderivatives. It examines the principles of option pricing in detail via standard binomial and stochasticcalculus models and develops the requisite mathe-matical background as needed. Numerous examplesand exercises help readers gain expertise with finan-cial calculus methods and increase their general math-ematical sophistication.

Features:

• Offers a straightforward account of the principles and models of option pricing

• Focuses on the (discrete time) binomial modeland the (continuous time) Black-Scholes-Mertonmodel

• Develops probability theory and finance theoryfrom first principles

• Covers various types of financial derivatives,including currency forwards, put and calloptions, and path-dependent options (Asian,lookback, and barrier options)

• Uses the notion of variation of a function to illustrate the similarities and differences betweenclassical 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:

Interest and Present Value. Probability Spaces.Random Variables. Options and Arbitrage. Discrete-Time Portfolio Processes. Expectation of a RandomVariable. The Binomial Model. ConditionalExpectation and Discrete-Time Martingales. TheBinomial Model Revisited. Stochastic Calculus. TheBlack-Scholes-Merton Model. Continuous-TimeMartingales. The BSM Model Revisited. OtherOptions. Appendices. Bibliography. Index.

Catalog no. K14090, November 2011, 266 pp.ISBN: 978-1-4398-8911-4, $59.95Also available as an eBook





StochasticFinanceA NumeraireApproachJan VecerColumbia University, New York,New York, USA


This classroom-tested text provides a deep under-standing of derivative contracts. Unlike much of theexisting literature, the book treats price as a numberof units of one asset needed for an acquisition of a unitof another asset instead of expressing prices in dollarterms exclusively. This numeraire approach leads tosimpler pricing options for complex products, such asbarrier, lookback, quanto, and Asian options. Withmany examples and exercises, the text relies on intu-ition and basic principles, rather than technical com-putations.

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 contin-

gent claims in terms of various reference assets• Presents examples of a model independent for-

mula for European call options; a simple methodfor pricing barrier options, lookback options,and Asian options; and a formula for options onLIBOR

• Provides prerequisite material on probability andstochastic calculus in the appendix

• Includes solutions to odd-numbered exercises atthe back of the book

Selected Contents:

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

Catalog no. K10632, January 2011, 342 pp.ISBN: 978-1-4398-1250-1, $69.95Also available as an eBook

QuantitativeFinanceAn Object-OrientedApproach in C++Erik SchloglUniversity of Technology, Sydney,Australia


A textbook for students and a reference guide for professionals, this text builds a foundation in the keymethods and models of quantitative finance from theperspective of their implementation in C++. It intro-duces computational finance in a pragmatic manner,focusing on practical implementation. The authortakes an object-oriented approach that starts fromsimple building blocks for assembling more complexand powerful models. The author expresses modelsand algorithms of the industry-standard C++ lan-guage and includes working C++ source code on aCD-ROM that accompanies the book.

Features:

• Presents quantitative finance in a pragmaticmanner with a focus on practical implementa-tion

• Serves as a self-contained reference for theimplementation of the key models and methods

• Expresses models and algorithms in the de factoindustry-standard programming language C++

• Takes an object-oriented approach, starting fromsimple building blocks to assemble more com-plex and powerful models

• Provides working C++ source code on CD-ROM

Selected Contents:

A Brief Review of the C++ Programming Language.Basic Building Blocks. Portfolio Optimization andAsset Pricing. Lattice Models. The Black/ScholesWorld. Finite Difference Methods for PartialDifferential Equations. Implied Volatility and ImpliedDistributions. Monte Carlo Simulation. TheHeath/Jarrow/Morton Model. The LognormalForward Rate "Market Models." Case Studies of theObject-Oriented Approach.

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



21

Statistics for Biological Sciences


Introduction toStatistical DataAnalysis for theLife SciencesClaus Thorn EkstrømUniversity of Copenhagen,Frederiksberg, Denmark

Helle SørensenUniversity of Copenhagen,Frederiksberg, Denmark

Any practical introduction to statistics in the life sci-ences requires a focus on applications and computa-tional statistics combined with a reasonable level ofmathematical rigor. It must offer the right combina-tion of data examples, statistical theory, and comput-ing required for analysis today. It should also involveR, the lingua franca of statistical computing.

Introduction to Statistical Data Analysis for the LifeSciences covers all the usual material but goes furtherthan 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 ofCopenhagen, the authors imbue readers with theability to model and analyze data early in the text andthen gradually fill in the blanks with needed probabil-ity and statistics theory. While the main text can beused with any statistical software, the authors encour-age a reliance on R. They provide a short tutorial forthose new to the software and include R commandsand output at the end of each chapter. Data sets usedin the book are available on a supporting website.

Each chapter contains a number of exercises, half ofwhich can be done by hand. The text also containsten case exercises where readers are encouraged toapply their knowledge to larger data sets and learnmore about approaches specific to the life sciences.Ultimately, readers come away with a computationaltoolbox that enables them to perform actual analysisfor real data sets as well as the confidence and skills toundertake more sophisticated analyses as their careersprogress.

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

Modelling andQuantitativeMethods inFisheriesSecond EditionMalcolm HaddonCSIRO, Hobart, Tasmania,Australia

With numerous real-world examples, Modelling andQuantitative Methods in Fisheries, Second Editionprovides an introduction to the analytical methodsused by fisheries’ scientists and ecologists. By follow-ing the examples using Excel, readers see the nuts andbolts of how the methods work and better under-stand the underlying principles. Excel workbooks areavailable for download from the CRC Press website.

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 ageneralized Gibbs sampler to run a Markovchain Monte Carlo analysis that can be used togenerate Bayesian posteriors

• Sized-Based Models implements a fully functionalsize-based stock assessment model usingabalone as an example

This book continues to cover a broad range of topicsrelated to quantitative methods and modelling. Itoffers a solid foundation in the skills required for thequantitative study of marine populations. Explainingimportant and relatively complex ideas and methodsin a clear manner, the author presents full, step-by-step derivations of equations as much as possible toenable a thorough understanding of the models andmethods.

Selected Contents:

Fisheries and Modelling. Simple Population Models.Model Parameter Estimation. Computer-IntensiveMethods. Randomization Tests. Statistical BootstrapMethods. Monte Carlo Modelling. Characterizationof Uncertainty. Growth of Individuals. StockRecruitment Relationships. Surplus ProductionModels. Age-Structured Models. Size-Based Models.Appendix. Bibliography. Index.

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




Statistics for Social Science & Psychology

Modern Statistics for the Socialand Behavioral SciencesA Practical IntroductionRand WilcoxUniversity 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 vol-ume with references, alternative procedures, and supporting software, the author has provided a valuableservice to those interested in these issues, which should probably include anyone teaching the techniques cov-ered in this book. … Recommended to those with a solid background in traditional statistical inference whowant a highly competent and comprehensive statement of the cases against traditional statistical inferencetechniques.”

—Robert W. Hayden, MAA Reviews, March 2012

In addition to learning how to apply classic statistical methods, students need to understand when these methodsperform well, and when and why they can be highly unsatisfactory. Modern Statistics for the Social andBehavioral Sciences illustrates how to use R to apply both standard and modern methods to correct known prob-lems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practicalproblems 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 introducesthree 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 availablefor 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 aredescribed.

• The deleterious effects of heteroscedasticity on conventional ANOVA and regression methods are muchmore serious than once thought. Effective techniques for dealing with heteroscedasticity are described andillustrated.

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 problemsassociated with classic techniques. Espousing the view that no single method is always best, it imparts a generalunderstanding 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 appendixSelected Contents:

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

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


CRC PressTaylor & Francis Group

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