Jan_2011 Statistics Textbooks

Contents

Statistics for Biology ..............................................3

Statistical Theory & Methods ................................5

Computational Statistics ......................................13

Statistics for Business & Finance ..........................14

Statistics for Engineering......................................16

Biostatistics ..........................................................17

Statistics in Psychology & Social Sciences............18

Environmental Statistics ......................................20

Statistics in Genetics & Biology............................21

Statistical Learning & Data Mining......................22

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3

Statistics for Biology

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New!

Modelling and Quantitative Methods in FisheriesSecond EditionMalcolm HaddonCSIRO, Hobart, Tasmania, Australia

Catalog no. C561X, February 2011, c. 471 pp.ISBN: 978-1-58488-561-0, $79.95

Praise for the First Edition

“The book is a good introduction to modeling forstudents and practitioners. … One helpful fea-ture is the use of spreadsheet examples to illus-trate the methods.”

—Fisheries, 2002

With numerous real-world examples, this textprovides students with an introduction to theanalytical methods used by fisheries’ scientistsand ecologists. By following the examples usingExcel, students see the nuts and bolts of how themethods work and better understand the under-lying principles. Excel workbooks will be avail-able for download from CRC Press Online.

In this second edition, the author has revised allchapters and improved a number of the exam-ples. This edition also includes two entirely newchapters:

• Characterization of Uncertainty covers asymptotic errors and likelihood profilesand develops a generalized Gibbs sampler torun a Markov chain Monte Carlo analysis thatcan be used to generate Bayesian posteriors

• Sized-Based Models implements a fully functional size-based stock assessment model using abalone as an example

This textbook continues to cover a broad rangeof topics related to quantitative methods andmodelling. It offers a solid foundation in theskills required for the quantitative study ofmarine populations. Explaining important andrelatively complex ideas and methods in a clearmanner, the author presents full, step-by-stepderivations of equations as much as possible toenable a thorough understanding of the modelsand methods.

Contents

Fisheries and ModellingFish Population DynamicsThe Objectives of Stock AssessmentCharacteristics of Mathematical ModelsTypes of Model StructureSimple Population ModelsAssumptions—Explicit and ImplicitDensity-Independent GrowthDensity-Dependent ModelsResponses to Fishing PressureThe Logistic Model in FisheriesAge-Structured ModelsSimple Yield-per-RecruitModel Parameter EstimationModels and DataLeast Squared ResidualsNonlinear EstimationLikelihoodBayes’ TheoremComputer-Intensive MethodsResamplingRandomization TestsJackknife MethodsBootstrapping MethodsMonte Carlo MethodsBayesian MethodsRelationships between MethodsComputer ProgrammingRandomization TestsStatistical Bootstrap MethodsMonte Carlo ModellingCharacterization of UncertaintyGrowth of IndividualsStock Recruitment RelationshipsSurplus Production ModelsAge-Structured ModelsSize-Based Models

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Statistics for Biology

New!

Introduction to Statistical Data Analysis for the Life SciencesClaus Thorn Ekstrom and Helle Sorensen

Catalog no. K11221, January 2011, 427 pp.Soft Cover, ISBN: 978-1-4398-2555-6, $69.95

University of Copenhagen, DenmarkDeveloped from the authors’ courses at theUniversity of Copenhagen, this textbook coversall the usual material but goes further than othertexts. The authors imbue students with the abilityto model and analyze data early in the text andthen gradually fill in the blanks with needed prob-ability and statistics theory.

While the main text can be used with any statisti-cal software, the authors encourage a reliance onR. They provide a short tutorial for students newto the software and include R commands andoutput at the end of each chapter. Ultimately, stu-dents come away with a computational toolboxthat enables them to perform actual analysis forreal data sets as well as the confidence and skillsto undertake more sophisticated analyses as theircareers progress.

Features

• Includes numerous exercises, half of which can be done by hand

• Contains ten case exercises that encouragestudents to apply their knowledge to largerdata sets and learn more about approachesspecific to the life sciences

• Offers a tutorial for students new to R • Provides data sets used in the text on a supporting website

• Emphasizes both data analysis and the mathematics underlying classical statisticalanalysis

• Covers modeling aspects of statistical analysis with added focus on biological interpretations

• Explores applications of statistical software in analyzing real-world problems and data sets

Solutions manual available for qualifying instructors

ContentsDescription of Samples and PopulationsData typesVisualizing categorical dataVisualizing quantitative dataStatistical summariesWhat is a probability?Linear RegressionComparison of GroupsGraphical and simple numerical comparisonBetween-group variation and within-group variationPopulations, samples, and expected valuesLeast squares estimation and residualsPaired and unpaired samplesPerspectiveThe Normal DistributionPropertiesOne sampleAre the data (approximately) normally distributed?The central limit theoremStatistical Models, Estimation, and ConfidenceIntervalsStatistical modelsEstimationConfidence intervalsUnpaired samples with different standard deviationsHypothesis TestsModel Validation and PredictionLinear Normal Models Multiple linear regressionAdditive two-way analysis of varianceLinear modelsInteractions between variablesProbabilitiesThe Binomial DistributionThe independent trials modelThe binomial distributionEstimation, confidence intervals, and hypothesis testsDifferences between proportionsAnalysis of Count DataLogistic Regression Case Exercises For more complete contents, visit www.crctextbooks.com


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Statistical Theory & Methods


Statistical Theory & Methods An Introduction to Statistical Inference and Its Applications with RMichael W. TrossetIndiana University, Bloomington, USA

Catalog no. C9470, 2009, 496 pp.ISBN: 978-1-58488-947-2, $79.95

This self-contained introduction helps studentsunderstand basic procedures of statistical infer-ence and how to use them. Emphasizing con-cepts rather than recipes, the text provides a clearexposition of the methods of statistical inferencefor students who are comfortable with mathe-matical notation. Numerous examples, case stud-ies, and exercises are included. R is used to sim-plify computation, create figures, and drawpseudorandom samples—not to perform entireanalyses.

The heart of the text is a careful exposition ofpoint estimation, hypothesis testing, and confi-dence intervals. The author also discusses the roleof simulation in modern statistical inference.

Features

• Explains how statistical methods are used for data analysis

• Uses the elementary functions of R to perform the individual steps of statistical procedures

• Includes amusing anecdotes and trivia, suchas Ambrose Bierce’s definition of insurance

• Introduces basic concepts of inferencethrough a careful study of several importantprocedures, including parametric and nonparametric methods, analysis of variance,and regression

• Presents many applications along with supporting data sets

• Contains exercises at the end of each chapter

• Offers the R code and data sets available fordownload online


ContentsExperiments Mathematical Preliminaries Probability Discrete Random VariablesContinuous Random Variables Quantifying Population Attributes Data The Plug-In Principle Plug-In Estimates of Mean and Variance Plug-In Estimates of Quantiles Kernel Density Estimates Case Study: Are Forearm Lengths NormallyDistributed? TransformationsLots of Data Averaging Decreases Variation The Weak Law of Large Numbers The Central Limit TheoremInferenceA Motivating Example Point EstimationHeuristics of Hypothesis Testing Testing Hypotheses about a Population MeanSet Estimation1-Sample Location Problems Case Study: Deficit Unawareness in Alzheimer’s Disease2-Sample Location ProblemsCase Study: Etruscan versus Italian Head BreadthThe Analysis of Variance Case Study: Treatments of AnorexiaGoodness-of-Fit Association Bivariate Distributions Normal Random Variables Monotonic Association Explaining Association Case Study: Anorexia Treatments RevisitedSimple Linear Regression Case Study: Are Thick Books More Valuable? Simulation-Based Inference Termite Foraging Revisited The Bootstrap Case Study: Adventure Racing



New!

Bayesian Ideas and Data AnalysisAn Introduction for Scientists and StatisticiansRonald ChristensenUniversity of New Mexico, Albuquerque, USA

Wesley O. JohnsonUniversity of California, Irvine, USA

Adam J. BranscumOregon State University, Corvallis, USA

Timothy E. HansonUniversity of South Carolina, Columbia, USA

Catalog no. K10199, January 2011, 516 pp.ISBN: 978-1-4398-0354-7, $69.95

Emphasizing the use of WinBUGS and R to ana-lyze real data, this text presents statistical tools toaddress scientific questions. It highlights founda-tional issues in statistics, the importance of mak-ing accurate predictions, and the need for scien-tists and statisticians to collaborate in analyzingdata. The WinBUGS code provided offers a con-venient platform to model and analyze a widerange of data.

Offering flexible options for teaching a variety ofcourses, the book focuses on the necessary toolsand concepts for modeling and analyzing scien-tific data. The first five chapters contain corematerial that spans basic Bayesian ideas, calcula-tions, and inference. The text also covers MonteCarlo methods, regression, survival analysis, bina-ry diagnostic testing, and nonparametric infer-ence.

Features

• Covers a large number of statistical models• Emphasizes the elicitation of reasonable prior information

• Explores numerical approximations via simulation

• Uses WinBUGS and R for computationalproblems

• Reviews basic concepts of matrix algebra and probability

• Includes numerous exercises and real-worldexamples throughout

• Provides data, programming code, and other materials on a supplemental website



Contents

Fundamental Ideas I Integration versus Simulation WinBUGS I: Getting Started Method of Composition Monte Carlo IntegrationPosterior Computations in RFundamental Ideas IIStatistical TestingExchangeability Likelihood Functions Sufficient Statistics Analysis Using Predictive Distributions Flat Priors Jeffreys’ Priors Bayes FactorsOther Model Selection CriteriaNormal Approximations to PosteriorsBayesian Consistency and Inconsistency Hierarchical Models Some Final Comments on LikelihoodsIdentifiability and Noninformative DataComparing Populations Illustrations: Foundry Data Simulations Generating Random Samples Traditional Monte Carlo MethodsBasics of Markov Chain TheoryMarkov Chain Monte CarloBasic Concepts of RegressionIllustration: FEV DataBinomial RegressionIllustrations: Space Shuttle DataLinear RegressionIllustrations: FEV DataCorrelated Data Illustrations: Interleukin DataCount Data Illustrations: Ache Hunting DataTime to Event DataIllustrations: Leukemia Cancer DataTime to Event Regression Illustrations: Leukemia Cancer DataBinary Diagnostic Tests Illustrations: Coronary Artery DiseaseNonparametric ModelsIllustrations: Galaxy DataFor more complete contents, visit www.crctextbooks.com


7



New!

NonparametricStatisticalInferenceFifth EditionJean Dickinson Gibbonsand Subhabrata ChakrabortiUniversity of Alabama,Tuscaloosa, USA

Praise for the Fourth Edition

“… a valuable addition to every statistician’slibrary.”

—ISI Short Book Reviews

“I learned nonparametric statistics … from thefirst author’s original version of the book. … aclassic text that should be part of every statisti-cian’s library. …”

—Technometrics, May 2004

“… a good textbook for a beginning graduate-level course in nonparametric statistics.”

—Journal of the American Statistical Association

Read more reviews at CRC Press Online

With at least 50 percent of the material revised,this edition covers the most commonly used non-parametric procedures. The authors carefully statethe assumptions, develop the theory behind theprocedures, and illustrate the techniques usingexamples from the social, behavioral, and life sci-ences. The text also contains many tables neededfor finding P values and obtaining confidenceinterval estimates of parameters.

Contents

Order Statistics, Quantiles, and Coverages.Tests of Randomness. Tests of Goodness of Fit.One-Sample and Paired-Sample Procedures.The General Two-Sample Problem. Linear RankStatistics and the General Two-SampleProblem. Linear Rank Tests for the LocationProblem. Linear Rank Tests for the ScaleProblem. Tests of the Equality of k IndependentSamples. Measures of Association for BivariateSamples. Measures of Association in MultipleClassifications. Asymptotic Relative Efficiency.Analysis of Count Data. Summary. Appendix ofTables. Answers to Problems. References. Index.

Catalog no. C7619, January 2011, 650 pp.ISBN: 978-1-4200-7761-2, $99.95

LogisticRegressionModelsJoseph M. HilbeJet Propulsion Laboratory,California Institute of Technology,Pasadena, and Arizona StateUniversity, Tempe, USA

This text shows students how to use logisticregression and extended logistic models forresearch. It presents an overview of the full rangeof logistic models, including binary, proportional,ordered, partially ordered, and unordered cate-gorical response regression procedures. Othertopics discussed include panel, survey, skewed,penalized, and exact logistic models.

Features

• Examines the theoretical foundation of many logistic models, including binary,ordered, multinomial, panel, and exact

• Describes how each type of model is established, interpreted, and evaluated as to its goodness of fit

• Analyzes the models using Stata• Offers R code at the end of most chapters so that students can duplicate the output displayed in the text

• Includes numerous exercises and real-worldexamples from the medical, ecological, physical, and social sciences

• Provides the example data sets in Stata, R,Excel, SAS, SPSS, and Limdep formats at CRC Press Online


Contents

Concepts Related to the Logistic Model.Estimation Methods. Derivation of the BinaryLogistic Algorithm. Model Development.Interactions. Analysis of Model Fit. BinomialLogistic Regression. Overdispersion. OrderedLogistic Regression. Multinomial LogisticRegression. Alternative Categorical ResponseModels. Panel Models. Other Types of Logistic-Based Models. Exact Logistic Regression.Conclusion. Appendices. References. Indices.






Design andAnalysis ofExperimentswith SASJohn LawsonBrigham Young University, Provo, Utah, USA

Covering both classical ideas in experimentaldesign and the latest research topics, this textprovides practical guidance on the computeranalysis of experimental data. It connects theobjectives of research to the type of experimentaldesign required, describes the actual process ofcreating the design and collecting the data,shows how to perform the proper analysis of thedata, and illustrates the interpretation of results.

Features

• Uses SAS 9.2 throughout to illustrate theconstruction of experimental designs andanalysis of data

• Shows how to display experimental resultsgraphically using SAS 9.2 ODS graphics

• Provides uniform coverage on experimentaldesigns and design concepts that are mostcommonly used in practice

• Presents many applications from the pharmaceutical, agricultural, industrial chemicals, and machinery industries

• Includes exercises at the end of every chapter• Offers all the SAS code for examples on theauthor’s website


Contents

Completely Randomized Designs with OneFactor. Factorial Designs. Randomized BlockDesigns. Designs to Study Variances. FractionalFactorial Designs. Incomplete and ConfoundedBlock Designs. Split-Plot Designs. Crossover andRepeated Measures Designs. Response SurfaceDesigns. Mixture Experiments. RobustParameter Design Experiments. ExperimentalStrategies for Increasing Knowledge.Bibliography. Index.


New!

Design ofExperimentsAn IntroductionBased on Linear ModelsMax MorrisIowa State University, Ames, USA

This text enables students to fully appreciate thefundamental concepts and techniques of experi-mental design as well as the real-world value ofdesign. It gives them a profound understandingof how design selection affects the informationobtained in an experiment.

Features

• Discusses the explicit relationship betweenexperimental design and the quality of dataanalysis

• Presents the fundamental concepts and techniques of experimental design

• Describes specific forms or classes of experimental designs

• Contains an introduction to design forregression models

• Performs calculations using R, with commands provided in an appendix

• Incorporates actual experiments drawn fromthe scientific and technical literature

• Includes many end-of-chapter exercises


Contents

Linear Statistical Models. CompletelyRandomized Designs. Randomized CompleteBlocks and Related Designs. Latin Squares andRelated Designs. Some Data Analysis for CRDsand Orthogonally Blocked Designs. BalancedIncomplete Block Designs. Random BlockEffects. Factorial Treatment Structure. Split-PlotDesigns. Two-Level Factorial Experiments:Basics. Two-Level Factorial Experiments:Blocking. Two-Level Factorial Experiments:Fractional Factorials. Factorial Group ScreeningExperiments. Regression Experiments: First-Order Polynomial Models. RegressionExperiments: Second-Order Polynomial Models.Introduction to Optimal Design. Appendices.References. Index.




9



AppliedStatisticalInference with MINITAB®

Sally A. LesikCentral Connecticut StateUniversity, New Britain, USA

“This book/CD-ROM package is intended for afirst course on applied inference for undergradu-ates and graduates in any field that uses statis-tics. The text is written to be beginner-friendlyand oriented toward practical use of statistics,with less emphasis on theory.”

—Book News, June 2010

Through clear, step-by-step mathematical calcu-lations, this text enables students to gain a solidunderstanding of how to apply statistical tech-niques using a statistical software program. Idealfor students in the social sciences, it shows how toimplement basic inferential techniques in practiceusing MINITAB®. The book establishes the foun-dation for students to build on work in moreadvanced inferential statistics.

Features

• Provides a complete integration of MINITABthroughout the text

• Includes fully worked out examples so students can easily follow the calculations

• Offers data sets and a trial version ofMINITAB on accompanying CD-ROMs

• Contains a set of homework problems at the end of each chapter


Contents

Graphing Variables. Descriptive Representationsof Data and Random Variables. Basic StatisticalInference. Simple Linear Regression. More onSimple Linear Regression. Multiple RegressionAnalysis. More on Multiple Regression. Analysisof Variance. Other Topics. Index.


Time SeriesModeling,Computation, and InferenceRaquel PradoUniversity of California, Santa Cruz, USA

Mike WestDuke University, Durham, North Carolina, USA

This text integrates mainstream approaches fortime series modeling with significant recentdevelopments in methodology and applicationsof time series analysis. It encompasses a graduate-level account of Bayesian time series modelingand analysis, state-of-the-art approaches to uni-variate and multivariate time series analysis, andemerging topics at research frontiers.

Features

• Covers the major areas of modern time series models and theory, including time and spectral domain and univariate and multivariate time series methods

• Presents analyses of real time series data innumerous examples and case studies to illustrate the flexibility and practical impact of the models and methods

• Discusses recent techniques for modelingtime series data, such as dynamic graphicalmodels, SMC methods, and nonlinear/non-Gaussian dynamic models

• Includes a collection of end-of-chapterexercises

• Offers many of the data sets, R and MATLAB®

code, and other material on the authors’websites

Contents

Notation, Definitions, and Basic Inference.Traditional Time Domain Models. TheFrequency Domain. Dynamic Linear Models.State-Space Time-Varying AutoregressiveModels. Sequential Monte Carlo Methods forState-Space Models. Mixture Models in TimeSeries. Topics and Examples in Multiple TimeSeries. Vector AR and ARMA Models.Multivariate DLMs and Covariance Models.Indices. Bibliography.






New! Introduction to General and Generalized Linear ModelsHenrik Madsen and Poul ThyregodTechnical University of Denmark, Lyngby


Providing a flexible framework for data analysisand model building, this text focuses on the sta-tistical methods and models that can help predictthe expected value of an outcome, dependent, orresponse variable. It offers a sound introductionto general and generalized linear models usingthe popular and powerful likelihood techniques.

Bridging the gap between theory and practice formodern statistical model building, the book cov-ers both general and generalized linear modelsusing the same likelihood-based methods. It pres-ents the corresponding/parallel results for thegeneral linear models first, since they are easier tounderstand and often more well known. Eachchapter contains examples and guidelines forsolving the problems via R, although other soft-ware packages are also discussed.

Features

• Enables a clear comparison between general and generalized linear models

• Provides an accessible description ofadvanced concepts of generalized linearmodels

• Covers Gaussian-based hierarchical modelsand hierarchical generalized linear models

• Introduces new concepts for mixed effectsmodels 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, additional exercises, datasets, and lecture slides on the book’s website

Contents

The Likelihood PrincipleGeneral Linear ModelsThe multivariate normal distribution General linear models Estimation of parameters Likelihood ratio tests Tests for model reduction Collinearity Inference on parameters in parameterized models Model diagnostics: residuals and influenceAnalysis of residuals Representation of linear models Generalized Linear ModelsTypes of response variables Exponential families of distributions Generalized linear models Maximum likelihood estimation Likelihood ratio tests Test for model reduction Inference on individual parameters Examples Mixed Effects ModelsGaussian mixed effects model One-way random effects model More examples of hierarchical variation General linear mixed effects models Bayesian interpretations Posterior distributions Random effects for multivariate measurements Hierarchical models in metrology General mixed effects models Laplace approximation Hierarchical ModelsIntroduction, approaches to modelling of overdispersion Hierarchical Poisson gamma model Conjugate prior distributions Examples of one-way random effects models Hierarchical generalized linear modelsReal-Life Inspired Problems Dioxin emission Depreciation of used cars Young fish in the North Sea Traffic accidents Mortality of snails



11



New!

Introduction to StatisticalLimit TheoryAlan M. PolanskyNorthern Illinois University,Dekalb, USA

Helping students develop a good understandingof asymptotic theory, this classroom-tested bookprovides a thorough yet accessible treatment ofcommon modes of convergence and their relatedtools used in statistics. It covers the necessaryintroductory material as well as modern statisticalapplications, exploring how the underlying math-ematical and statistical theories work together.

Features

• Presents a review of the relevant mathematicallimit theory that is used throughout the book

• Provides coverage of expansion theory, atopic not typically covered in asymptotic texts

• Incorporates detailed proofs and explanationsof the results

• Uses examples to illustrate the application of asymptotic theory to modern statisticalproblems

• Offers references for further reading as wellas tips on using R as a tool for visualizingasymptotic results

• Includes many end-of-chapter exercises andexperiments, ranging in level of difficultyfrom easy to advanced

Forthcoming solutions manual available for qualifying instructors

Contents

Sequences of Real Numbers and Functions.Random Variables and Characteristic Functions.Convergence of Random Variables.Convergence of Distributions. Convergence ofMoments. Central Limit Theorems. AsymptoticExpansions for Distributions. AsymptoticExpansions for Random Variables. DifferentiableStatistical Functionals. Parametric Inference.Nonparametric Inference. Appendices.References.


RandomPhenomenaFundamentals ofProbability andStatistics forEngineersBabatunde A. OgunnaikeUniversity of Delaware, Newark, USA

“…an excellent choice for anyone, educator orpractitioner, wishing to impart or gain a funda-mental understanding of probability and statis-tics from an engineering perspective.”

—Dennis C. Williams, The American Institute ofChemical Engineers Journal

Features

• Includes case studies on Mendel’s study ofgenetics, conditional probabilities in WorldWar II Naval tactical decision making, and in vitro fertilization

• Provides examples drawn from molecularbiology, finance and business, and population demographics

• Supplies review questions, exercises, application problems, and project assignments

• Presents data sets online and on CD-ROM,with a 30-day MINITAB trial featuringreduced purchase/rental rate offer


Contents

FOUNDATIONS: Two Motivating Examples.Random Phenomena, Variability, and Uncertainty.PROBABILITY: Fundamentals of ProbabilityTheory. Random Variables and Distributions.Multidimensional Random Variables. RandomVariable Transformations. Application CaseStudies I: Probability. DISTRIBUTIONS: IdealModels of Discrete Random Variables. IdealModels of Continuous Random Variables.Information, Entropy, and Probability Models.Application Case Studies II: In Vitro Fertilization.STATISTICS: Introduction to Statistics. Sampling.Estimation. Hypothesis Testing. RegressionAnalysis. Probability Model Validation.Nonparametric Methods. Design of Experiments.Application Case Studies III: Statistics. APPLICATIONS: Reliability and Life Testing.Quality Assurance and Control. Introduction toMultivariate Analysis. Appendix. Index.

Catalog no. 44974, 2010, 1056 pp.ISBN: 978-1-4200-4497-3, $133.95





StochasticProcessesAn Introduction,Second EditionPeter W. Jones andPeter SmithKeele University, Staffordshire, UK

Based on the authors’ highly popular, well-estab-lished course, this concise, updated textbook dis-cusses the modeling and analysis of randomexperiments using the theory of probability. Theauthors make the material accessible to studentsby avoiding specialized applications and insteadhighlighting simple applications and examples.The associated website contains Mathematica®

and R programs that offer flexibility in creatinggraphs and performing computations.

Features

• Illustrates discrete random processes throughthe classical gambler’s ruin problem and itsvariants

• Covers continuous random processes, such as Poisson and general population models

• Describes applications of probability to modeling problems in engineering, medicine,and biology

• Uses Mathematica and R to solve both theoretical and numerical examples and produce many graphs

• Includes over 50 worked examples and morethan 200 end-of-chapter problems withselected answers at the back of the book

• Provides Mathematica and R programs on thebook’s website


Contents

Some Background on Probability. SomeGambling Problems. Random Walks. MarkovChains. Poisson Processes. Birth and DeathProcesses. Queues. Reliability and Renewal.Branching and Other Random Processes.Computer Simulations and Projects. Answersand Comments on End-of-Chapter Problems.Appendix. References and Further Reading.Index.

Catalog no. K10004, 2010, 232 pp., Soft CoverISBN: 978-1-4200-9960-7, $82.95

Modeling andAnalysis ofStochasticSystemsSecond EditionVidyadhar G. KulkarniUniversity of North Carolina,Chapel Hill, USA

“… an accessible, well paced, and very nicelypresented book. The publishers are also to becommended on its nice production: it is the sortof book which is a pleasure to read. In all, it is anexcellent textbook for use in introductory courseson stochastic processes.”

—International Statistical Review (2010), 78, 3


This book covers the most important classes ofstochastic processes used in the modeling ofdiverse systems. After mastering the material inthe text, students will be well-equipped to buildand analyze useful stochastic models for varioussituations.

Along with new appendices that collect resultsfrom analysis and differential and difference equa-tions, this edition contains a new chapter on dif-fusion processes with applications to finance. Italso offers a more streamlined, application-orient-ed approach to renewal, regenerative, andMarkov regenerative processes. MATLAB®-basedprograms can be downloaded from the author’swebsite and a solutions manual is available forqualifying instructors.

Contents

Discrete-Time Markov Chains: TransientBehavior. DTMCs: First Passage Times. DTMCs:Limiting Behavior. Poisson Processes.Continuous-Time Markov Chains. QueueingModels. Renewal Processes. MarkovRegenerative Processes. Diffusion Processes.Epilogue. Appendices. Answers to SelectedProblems. References. Index.

Catalog no. K10430, 2010, 563 pp.ISBN: 978-1-4398-0875-7, $99.95



13

Computational Statistics


ComputationalStatistics Graphics forStatistics and DataAnalysis with RKevin J. KeenUniversity of Northern British Columbia, Prince George,Canada

“The book is methodical and complete …Reading this book will give you the ability to rec-ognize and create the majority of the namedgraphics of statistics …”

—Journal of Statistical Software, September 2010, Volume 36

Showing students how to use graphics to displayor summarize data, this text provides best prac-tice guidelines for producing and choosingamong graphical displays. It also covers the mosteffective graphing functions in R.The author presents the basic principles of soundgraphical design and applies these principles toexamples using the graphical functions availablein R. It offers a wide array of graphical displays forthe presentation of data, including modern toolsfor data visualization and representation.

Features

• Emphasizes the fundamentals of statisticalgraphics

• Describes the strengths and weaknesses ofeach graphical display in R

• Presents technical theoretical details on topics such as the estimation of quantiles,kernel density estimation, locally weightedpolynomial regression, and splines

• Includes engaging examples of real-worlddata, end-of-chapter exercises, and manyillustrations, with some in color

• Provides downloadable R code and data forthe figures in the text on the book’s website

Contents

A Single Discrete Variable. A Single ContinuousVariable. Two Variables. Statistical Models forTwo or More Variables. References. Index.


ComputationalStatisticsAn Introduction to RGünther SawitzkiStatLab, Heidelberg, Germany

“… a fresh perspective on teaching statistics. …The book introduces its topics and the corre-sponding methodologies well. … the book is wellput together and quite enjoyable for its purposeof serving a small course on computational sta-tistics. …”

—Journal of Statistical Software, December 2009

“… it is the integration of interesting examplesand associated R code that make the text apleasure to read and work through. The exam-ples are neither overly trivial … nor excessivelycomplicated, and the R code is similarly accessi-ble without being either too simple or complex.…”

—Ronald D. Fricker, Jr., The American Statistician

Using a range of examples, this introductionshows students how R can be employed to tacklestatistical problems. A handy appendix includes acollection of R language elements and functions,serving as a quick reference and starting point toaccess the rich information that comes bundledwith R. Helping students become familiar with R,the author offers the full R source code for allexamples, selected solutions, and other materialon the book’s website.

Contents

Basic Data Analysis. Regression. Comparisons.Dimensions 1, 2, 3, …, Infinity. R as aProgramming Language and Environment.References. Functions and Variables by Topic.Function and Variable Index. Subject Index.





Statistics for Business & Finance

New!

Stochastic FinanceA Numeraire ApproachJan VecerColumbia University, New York, New York, USA


“… this is the first book to stress the fundamen-tal role that numeraires play in modern assetpricing theory. The author is the leading experton the subject so it is a pleasure to highly recom-mend this book.”

—Peter Carr, Ph.D., Managing Director of Morgan Stanley, and Executive Director of

NYU’s Masters in Math Finance

“Finally, we have a full volume with a systematictreatment of the change of numeraire tech-niques. Jan Vecer has taken years of teachingexperience and practitioners’ feedback to unify apreviously complicated topic into the most ele-gant and easily accessible numeraire textbook tocome down the pike. …”

—Uwe Wystup, Ph.D., Managing Director of MathFinance AG

This classroom-tested text provides a deep under-standing of derivative contracts. It treats price asa number of units of one asset needed for anacquisition of a unit of another asset instead ofexpressing prices in dollar terms exclusively. Thisnumeraire approach leads to simpler pricingoptions for complex products.

Features

• Focuses on fundamental principles of pricing• Shows students how to identify the basicassets of a given contract

• Explains how to compute the prices of contingent claims in terms of various reference assets

• Presents examples of a model independentformula for European call options; a simplemethod for pricing barrier options, lookbackoptions, and Asian options; and a formula foroptions on LIBOR

• Provides prerequisite material on probabilityand stochastic calculus in the appendix

• Includes solutions to odd-numbered exercisesat the back of the book

Contents

Elements of FinanceBinomial Models Diffusion Models Interest Rate Contracts Forward LIBOR Swaps and Swaptions Term Structure ModelsBarrier Options Types of Barrier Options Barrier Option Pricing via Power Options Price of a Down-and-In Call Option Connections with the Partial DifferentialEquationsLookback OptionsConnections of Lookbacks with Barrier OptionsPartial Differential Equation Approach forLookbacks Maximum DrawdownAmerican OptionsAmerican Options on No-Arbitrage Assets American Call and Puts on Arbitrage Assets Perpetual American Put Partial Differential Equation ApproachContracts on Three or More Assets: Quantos,Rainbows and “Friends” Pricing in the Geometric Brownian MotionModel HedgingAsian OptionsPricing in the Geometric Brownian MotionModel Hedging of Asian Options Reduction of the Pricing EquationsJump Models Poisson Process Geometric Poisson Process Pricing Equations European Call Option in Geometric PoissonModelLévy Models with Multiple Jump Sizes



15

Statistics for Business & Finance


AppliedStatistics forBusiness andEconomicsRobert M. LeekleyIllinois Wesleyan University,Bloomington, USA

“…this text does an outstanding job of integratingthings on the mathematical level. … [Students] willlike the clear and to the point writing.”

—MAA Reviews, September 2010

Designed for a one-semester course, this textoffers students in business and the social sciencesan effective introduction to some of the most basicand powerful techniques available for understand-ing their world. After reading the book, studentswill be able to summarize data in insightful waysusing charts, graphs, and summary statistics aswell as make inferences from samples.

Numerous interesting and important examplesreflect real-life situations and calculations can beperformed using any standard spreadsheet pack-age. To help with the examples, the author offersboth actual and hypothetical databases on hiswebsite. A solutions manual is available for quali-fying instructors.

Contents

Describing Data: Tables and Graphs. DescribingData: Summary Statistics. Basic Probability.Probability Distributions. Sampling andSampling Distributions. Estimation andConfidence Intervals. Tests of Hypotheses: One-Sample Tests. Tests of Hypotheses: Two-SampleTests. Tests of Hypotheses: Contingency andGoodness-of-Fit. Tests of Hypotheses: ANOVAand Tests of Variances. Simple Regression andCorrelation. Multiple Regression. Time-SeriesAnalysis. Appendices. Index.


StochasticFinancialModelsDouglas KennedyTrinity College, Cambridge, UK

“This book is a superb beginning level text forsenior undergraduate/graduate mathemati-cians, which is based on lectures delivered by itsauthor to many generations of appreciativeCambridge mathematicians. Many of my ownPh.D. and masters students have taken Dr.Kennedy’s course to uniformly good reviews; thisreadable book will make its material available toa worldwide audience. … the book contains 40pages of fully worked out solutions … .”

—M.A.H. Dempster, Centre for Financial Research,Statistical Laboratory, University of Cambridge, UK

Developed from the esteemed author’s advancedundergraduate and graduate courses at theUniversity of Cambridge, this text provides ahands-on, sound introduction to mathematicalfinance. It is suitable for students at different lev-els of mathematical maturity.

Assuming no prior knowledge of stochastic calcu-lus or measure-theoretic probability, the authorincludes the relevant mathematical backgroundas well as many exercises with solutions. He takesa hands-on approach to calculations, enablingstudents to derive the prices of many commonfinancial products.

Contents

Portfolio Choice. The Binomial Model. AGeneral Discrete-Time Model. BrownianMotion. The Black–Scholes Model. Interest-RateModels. Solutions. Appendices. FurtherReading. References. Index.





Statistics for Engineering

New!

TransportationStatistics andMicrosimulationClifford H. SpiegelmanTexas A&M University, College Station, USA

Eun Sug ParkTexas Transportation Institute,College Station, USA

Laurence R. RilettUniversity of Nebraska, Lincoln, USA

By discussing statistical concepts in the context oftransportation planning and operations,Transportation Statistics and Microsimulationprovides students with the necessary backgroundfor making informed transportation-related deci-sions. It explains the why behind standard meth-ods and uses real-world transportation examplesand problems to illustrate key concepts.

Classroom-tested at Texas A&M University, thetext covers the statistical techniques most fre-quently employed by transportation and pave-ment professionals. To familiarize students withthe underlying theory and equations, it containsproblems that can be solved using statistical soft-ware. The authors encourage the use of SAS’s JMPpackage, which enables users to interactivelyexplore and visualize data. Students can buy theirown copy of JMP at a reduced price via a postcardin the book.

Contents

Overview: The Role of Statistics inTransportation Engineering. Graphical Methodsfor Displaying Data. Numerical SummaryMeasures. Probability and Random Variables.Common Probability Distributions. SamplingDistributions. Inferences: Hypothesis Testingand Interval Estimation. Other InferentialProcedures: ANOVA and Distribution-Free Tests.Inferences Concerning Categorical Data. LinearRegression. Regression Models for Count Data.Experimental Design. Cross-Validation,Jackknife, and Bootstrap Methods for ObtainingStandard Errors. Bayesian Approaches toTransportation Data Analysis. Microsimulation.Appendix.


New!

Statistical andEconometricMethods forTransportationData AnalysisSecond EditionSimon P. WashingtonMatthew G. KarlaftisFred L. Mannering


“… the definitive text on statistics in transportation for some years to come …”

—Technometrics, November 2004

“… an outstanding and unique contribution tothe existing transportation literature. … an excellent textbook for a number of graduate-levelclasses in transportation-related disciplines.”

—Journal of Transportation Engineering,September/October 2004


With many examples and case studies, this best-selling text teaches students how to solve trans-portation problems using a range of analyticaltools. This edition includes new chapters on logis-tic regression, ordered probability models, ran-dom-parameter models, and Bayesian statisticalmodeling. Data sets and PowerPoint and Wordpresentations are available online.

Contents

FUNDAMENTALS: Statistical Inference I:Descriptive Statistics. Statistical Inference II:Interval Estimation, Hypothesis Testing, andPopulation Comparisons. CONTINUOUSDEPENDENT VARIABLE MODELS: LinearRegression. Violations of RegressionAssumptions. Simultaneous-Equation Models.Panel Data Analysis. Background and Explorationin Time Series. Forecasting in Time Series:Autoregressive Integrated Moving Average(ARIMA) Models and Extensions. Latent VariableModels. Duration Models. COUNT AND DIS-CRETE DEPENDENT VARIABLE MODELS: CountData Models. Logistic Regression. DiscreteOutcome Models. Ordered Probability Models.Discrete/Continuous Models. OTHER STATISTI-CAL METHODS: Random-Parameter Models.Bayesian Models. Appendices. References. Index.

Catalog no. C285X, January 2011, 544 pp.ISBN: 978-1-4200-8285-2, $99.95



17

Biostatistics


New!

Exercises and Solutions in Biostatistical TheoryLawrence L. KupperUniversity of North Carolina, Chapel Hill, USA

Brian NeelonDuke University, Durham, North Carolina, USA

Sean M. O’BrienDuke University School of Medicine, Durham, North Carolina, USA

Catalog no. C7222, January 2011, 420 pp.Soft Cover, ISBN: 978-1-58488-722-5, $49.95

Drawn from nearly four decades of Lawrence L.Kupper’s teaching experiences as a distinguishedprofessor in the Department of Biostatistics at theUniversity of North Carolina, Exercises andSolutions in Biostatistical Theory presents theo-retical statistical concepts, numerous exercises,and detailed solutions that span topics from basicprobability to statistical inference. The text linkstheoretical biostatistical principles to real-world sit-uations, including some of the authors’ own bio-statistical work that has addressed complicateddesign and analysis issues in the health sciences.

This classroom-tested material is arranged sequen-tially starting with a chapter on basic probabilitytheory, followed by chapters on univariate distri-bution theory and multivariate distribution theory.The last two chapters on statistical inference coverestimation theory and hypothesis testing theory.Each chapter begins with an in-depth introductionthat summarizes the biostatistical principles need-ed to help solve the exercises. Exercises range inlevel of difficulty from fairly basic to more chal-lenging.

By working through the exercises and detailedsolutions in this book, students will develop a deepunderstanding of the principles of biostatisticaltheory. The text shows how the biostatistical the-ory is effectively used to address important biosta-tistical issues in a variety of real-world settings.Mastering the theoretical biostatistical principlesdescribed in the book will prepare students forsuccessful study of higher-level statistical theoryand will help them become better biostatisticians.

Contents

Basic Probability TheoryUnivariate Distribution TheoryDiscrete and Continuous Random Variables Cumulative Distribution Functions Median and Mode Expectation Theory Some Important Expectations Inequalities Involving ExpectationsSome Important Probability Distributions forDiscrete Random VariablesSome Important Distributions for ContinuousRandom VariablesMultivariate Distribution TheoryDiscrete and Continuous MultivariateDistributionsMultivariate Cumulative Distribution Functions Expectation TheoryMarginal Distributions Conditional Distributions and Expectations Mutual Independence among a Set of RandomVariables Random Sample Some Important Multivariate Discrete andContinuous Probability DistributionsSpecial Topics of Interest Estimation TheoryPoint Estimation of Population Parameters Data Reduction and Joint Sufficiency Methods for Evaluating the Properties of a PointEstimator Interval Estimation of Population ParametersHypothesis Testing TheoryBasic Principles Most Powerful (MP) and Uniformly MostPowerful (UMP) Tests Large-Sample ML-Based Methods for Testing aSimple Null Hypothesis versus a CompositeAlternative Hypothesis Large-Sample ML-Based Methods for Testing aComposite Null Hypothesis versus a CompositeAlternative Hypothesis

For more complete contents, visit www.crcpress.com



Statistics in Psychology & Social Sciences

Statistics in Psychology & Social Sciences Applied Survey Data AnalysisSteven G. Heeringa, Brady T. West, and Patricia A. BerglundUniversity of Michigan, Ann Arbor, USA


“… there is a wealth of instruction here. Thewriting style is expansive, keeping mathematicsin check, and the material is well organizedclearly into appropriate sections. I think that thebook would serve any budding survey practition-er well: armed with the knowledge and practicalskills covered herein, plus some real-life experi-ence of course, one could reasonably claim to bewell qualified in the subject.”

—International Statistical Review (2010), 78, 3

This text provides a practical, intermediate-levelstatistical overview of the analysis of complexsample survey data. It emphasizes methods andworked examples using available software proce-dures while reinforcing the principles and theorythat underlie those methods. The book containsmany examples and practical exercises based onmajor real-world survey data sets. Although theauthors use Stata for most examples in the text,they offer SAS, SPSS, SUDAAN, R, WesVar,IVEware, and Mplus software code for replicatingthe examples on the book’s website.

The authors introduce a step-by-step process forapproaching a survey analysis problem, presentthe fundamental features of complex sampledesigns, and show how to integrate design char-acteristics into statistical methods and software.They also cover novel developments in surveyapplications of advanced statistical techniques,including model-based analysis approaches.

Features

• Demonstrates how design characteristics, suchas stratification, clustering, and weighting, are easily incorporated into the statistical methods and software for survey estimation and inference

• Presents many methods and models for urvey data analysis, including the linear regression, generalized linear, Cox proportionalhazards, and discrete time models

• Explores developments in advanced statisticaltechniques, such as multilevel analysis of urvey data

• Supplies advice and recommendations basedon the authors’ experiences as well as currentthinking on best practices

• Uses theory boxes to develop or explain a fundamental theoretical concept underlyingstatistical methods

• Includes practical exercises that reinforce application of the methods

• Offers software code, brief technical reports,links to example survey data sets, and more on the book’s website

Contents

Applied Survey Data Analysis: Overview. Gettingto Know the Complex Sample Design.Foundations and Techniques for Design-BasedEstimation and Inference. Preparation forComplex Sample Survey Data Analysis.Descriptive Analysis for Continuous Variables.Categorical Data Analysis. Linear RegressionModels. Logistic Regression and GeneralizedLinear Models for Binary Survey Variables.Generalized Linear Models for Multinomial,Ordinal, and Count Variables. Survival Analysis ofEvent History Survey Data. Multiple Imputation:Methods and Applications for Survey Analysts.Advanced Topics in the Analysis of Survey Data.References. Appendix.


19

Statistics in Psychology & Social Sciences


Foundations ofFactor AnalysisSecond EditionStanley A. MulaikGeorgia Institute of Technology,Atlanta, USA

“… I must say that I am very happy that theauthor has taken the challenge to update andrevise this precious book into the second edition.… the topics are explained clearly, and mathe-matics is taught as it is needed to understand aderivation of an equation or some procedure. …the book is worth having nearby … .”

—International Statistical Review (2010), 78

Providing a practical, thorough understanding ofhow factor analysis works, this textbook enablesstudents to choose the proper factor analytic pro-cedure, make modifications to the procedure,and produce new results. This long-awaited sec-ond edition includes a new chapter on the multi-variate normal distribution, a rewritten chapter onanalytic oblique rotation, and a revised chapteron confirmatory factor analysis. This edition alsooffers more complete coverage of descriptive fac-tor analysis and doublet factor analysis andexplores the developments of factor score inde-terminacy. SPSS programs are available for down-load at CRC Press Online.

Contents

Mathematical Foundations for Factor Analysis.Composite Variables and LinearTransformations. Multiple and PartialCorrelations. Multivariate Normal Distribution.Fundamental Equations of Factor Analysis.Methods of Factor Extraction. Common-FactorAnalysis. Other Models of Factor Analysis.Factor Rotation. Orthogonal Analytic Rotation.Oblique Analytic Rotation. Factor Scores andFactor Indeterminacy. Factorial Invariance.Confirmatory-Factor Analysis. References.Indices.


MultivariableModeling andMultivariateAnalysis for theBehavioralSciencesBrian S. EverittKing’s College, University of London, UK

“… Everitt successfully crafts a well-integratedintroductory text that obviates potential difficultiesby including real problems and their data sets. …”

—Psychometrika, June 2010

“… Especially the second chapter, which showshow to look at data, is among the best I haveever seen in books on multivariate methods. …He also goes well beyond the typical graphs,showing how to explore real insights from thedata. … the book is extremely easy to browseand read. … I’ll be happy to recommend thisbook to students and researchers.”

—International Statistical Review, 2010

With many real-world examples, graphs, andexercises, this text equips students with the rightstatistical tools for analyzing data. The author sep-arates mathematical details from the main textand removes the burden of performing necessarycalculations by encouraging the use of R.Solutions to the problems as well as all R code and data sets for the examples are available atCRC Press Online.

Contents

Data, Measurement, and Models. Looking atData. Simple Linear and Locally WeightedRegression. Multiple Linear Regression. TheEquivalence of Analysis of Variance and MultipleLinear Regression, and An Introduction to theGeneralized Linear Model. Logistic Regression.Survival Analysis. Linear Mixed Models forLongitudinal Data. Multivariate Data andMultivariate Analysis. Principal ComponentsAnalysis. Factor Analysis. Cluster Analysis.Grouped Multivariate Data. References.Appendix. Index.

Catalog no. K10396, 2010, 320 pp.Soft Cover, ISBN: 978-1-4398-0769-9, $71.95



Environmental and Ecological Statistics with RSong S. QianNicholas School of the Environment, Duke University, Durham, North Carolina, USA

Catalog no. C6206, 2010, 440 pp., Soft CoverISBN: 978-1-4200-6206-9, $82.95

Emphasizing the inductive nature of statisticalthinking, Environmental and EcologicalStatistics with R connects applied statistics to theenvironmental and ecological fields. It follows thegeneral approach to solving a statistical modelingproblem, covering model specification, parame-ter estimation, and model evaluation. The authoruses many examples to illustrate the statisticalmodels and presents R implementations of themodels.

The book first builds a foundation for conductinga simple data analysis task, such as exploratorydata analysis and fitting linear regression models.It then focuses on statistical modeling, includinglinear and nonlinear models, classification andregression tree, and the generalized linear model.The text also discusses the use of simulation formodel checking, provides tools for a criticalassessment of the developed model, and exploresmultilevel regression models, which are a class ofmodels that can have a broad impact in environ-mental and ecological data analysis.

Based on courses taught by the author at DukeUniversity, this textbook focuses on statisticalmodeling and data analysis for environmentaland ecological problems. By guiding studentsthrough the processes of scientific problem solv-ing and statistical model development, it easesthe transition from scientific hypothesis to statisti-cal model.

Features

• Describes each type of statistical modelthrough examples

• Explains how to conduct data analysis

• Discusses simulation for model checking, an important aspect of model developmentand assessment

• Presents multilevel regression models, such as multilevel ANOVA, multilevel linear regression, and generalized multilevel

• Shows students how the methods can beimplemented using R

• Offers the data sets and R scripts used in the book along with exercises and solutionson the author’s website

Contents

BASIC CONCEPTSIntroduction. R. Statistical Assumptions. StatisticalInference.

STATISTICAL MODELINGLinear Models. Nonlinear Models. Classificationand Regression Tree. Generalized Linear Model.

ADVANCED STATISTICAL MODELINGSimulation for Model Checking and StatisticalInference. Multilevel Regression. ReferencesIndex



Environmental Statistics


21

Statistics in Genetics & Biology


Statistics in Human Genetics and Molecular BiologyCavan ReillyUniversity of Minnesota, Minneapolis, USA

“Very useful for those taking courses in statistics and geneticists.”—Pediatric Endocrinology Reviews, Vol. 7, No. 4, June 2010


Focusing on the roles of different segments ofDNA, Statistics in Human Genetics and MolecularBiology provides students with a basic under-standing of problems arising in the analysis ofgenetics and genomics. It presents statisticalapplications in genetic mapping, DNA/proteinsequence alignment, and analyses of geneexpression data from microarray experiments.

The text introduces a diverse set of problems anda number of approaches that have been used toaddress these problems. It discusses basic molec-ular biology and likelihood-based statistics, alongwith physical mapping, markers, linkage analysis,parametric and nonparametric linkage, sequencealignment, and feature recognition. The text illus-trates the use of methods that are widespreadamong researchers who analyze genomic data,such as hidden Markov models and the extremevalue distribution. It also covers differential geneexpression detection as well as classification andcluster analysis using gene expression data sets.

With worked examples and end-of-chapter exer-cises, this text presents various approaches tohelp students solve problems at the interface ofstatistics, biostatistics, computer science, andrelated fields in applied mathematics.

Contents

Basic Molecular Biology for Statistical Geneticsand Genomics Basics of Likelihood-Based StatisticsMarkers and Physical Mapping Basic Linkage Analysis Extensions of the Basic Model for ParametricLinkage Nonparametric Linkage and AssociationAnalysis

Introduction Sib-pair method Identity by descent Affected sib-pair (ASP) methodsQTL mapping in human populationsA case study: dealing with heterogeneity inQTL mapping Linkage disequilibrium Association analysisSequence Alignment Significance of Alignments and Alignment inPracticeHidden Markov Models Feature Recognition in Biopolymers Gene transcription Detection of transcription factor binding sites Computational gene recognitionMultiple Alignment and Sequence FeatureDiscovery Dynamic programmingProgressive alignment methodsHidden Markov modelsBlock motif methodsEnumeration based methods A case study: detection of conserved elementsin mRNAStatistical GenomicsFunctional genomics The technology Spotted cDNA arrays Oligonucleotide arrays NormalizationDetecting Differential Expression Multiple testing and the false discovery rate Significance analysis for microarrays Model based empirical Bayes approach A case study: normalization and differentialdetectionCluster Analysis in Genomics Classification in Genomics



New!

Exploratory Data Analysis with MATLAB®

Second EditionWendy L. MartinezThe Department of Defense, Fredericksburg, Virginia, USA

Angel R. MartinezStrayer University, Fredericksburg, Virginia, USA

Jeffrey L/ SolkaThe Department of the Navy, Dahlgren, Virginia, USA



“… I found the book to be engagingly written,and successful in its defined task of teaching thereader to use EDA with MATLAB. I liked thegraphics and thought that they fully illustratedthe techniques used.”

—Brian Jersky, Sonoma State University, Journal of the American Statistical Association


Covering innovative approaches for dimensionali-ty reduction, clustering, and visualization, this textuses numerous examples and applications to showstudents how the methods are used in practice.

New to the Second Edition

• Discussions of nonnegative matrix factorization, linear discriminant analysis,curvilinear component analysis, independentcomponent analysis, and smoothing splines

• An expanded set of methods for estimatingthe intrinsic dimensionality of a data set

• Several clustering methods, including probabilistic latent semantic analysis andspectral-based clustering

• Additional visualization methods, such as a rangefinder boxplot, scatterplots with marginal histograms, biplots, and a newmethod called Andrews’ images

• Instructions on a free MATLAB® GUI toolboxfor EDA

Like its predecessor, this edition continues tofocus on using EDA methods, rather than theo-retical aspects. The MATLAB codes for the exam-ples, EDA toolboxes, data sets, and color versionsof all figures are available for download online.

Features

• Shows how to use EDA methods via examplesand applications

• Covers state-of-the-art techniques for dimensionality reduction, clustering, and visualization

• Provides MATLAB code for virtually all algorithms covered in the text

• Includes pseudo-code to implement algorithmsin software other than MATLAB

• Describes many functions of the GUI toolboxfor EDA

• Contains an eight-page color insert illustratingdata output from several MATLAB examples

Contents

INTRODUCTION TO EXPLORATORY DATA ANALYSISIntroduction to Exploratory Data Analysis

EDA AS PATTERN DISCOVERYDimensionality Reduction - Linear Methods.Dimensionality Reduction - Nonlinear Methods.Data Tours. Finding Clusters. Model-BasedClustering. Smoothing Scatterplots.

GRAPHICAL METHODS FOR EDAVisualizing Clusters. Distribution Shapes.Multivariate Visualization.Appendices ReferencesIndex


Statistical Learning & Data Mining


Ensure your students keep up with cutting-edge theory and applications of MCMC

www.crctextbooks.com

Visit CRC Press Online for more informationand to view the complete table of contents.

• Thorough coverage of the theoretical foundationsand algorithmic and computational methodologymake constructing, implementing, and choosingMCMC techniques easier than ever

• In-depth introductory section allows students newto MCMC to become thoroughly acquainted withthe basic theory, algorithms, and applications

• Detailed examples and case studies ofrealistic scientific problems showcase the diversity of methods used by thewide-ranging MCMC community

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Jan_2011 Statistics Textbooks