Statistics

Textbooks

Statistics

Teach with the books you trust.™

Request your complimentary inspection copy today!

WWW.CRCTEXTBOOKS.COM CRC PressTaylor & Francis Group

CRC PressTextbooks

http://www.crcpress.com/textbooks/

http://www.crcpress.com/textbooks/browse/STA

CONTENTS

Introductory Statistics..............................................3

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

Computational Statistics ......................................12

Biostatistics & Epidemiology ................................14

Statistics for Engineering & Physical Science ........15

Statistics for Business, Finance, & Economics........17

Statistics for Biological Sciences ............................20

Statistics for Social Science & Psychology ............22

Page 5 Page 7

Page 10 Page 12

Page 14 Page 21

GTN18 TMC

For more information and complete tables of contents

or to request your complimentary inspection copy

visit

www.crctextbooks.com1-800-634-7064 • 1-859-727-5000

+44 (0) 1235 400 [email protected] Press Textbooks

Teach with the booksyou trust.™



https://www.facebook.com/CRCPresspage

http://www.linkedin.com/groups/CRC-Press-Authors-Editors-2234366/about

http://www.youtube.com/crcpresstandf

http://twitter.com/crcpress

mailto:[email protected]

http://issuu.com/action/page?page=3










http://issuu.com/action/page?page=

http://issuu.com/action/page?page=



3

Introductory Statistics

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

New!

Using R for Introductory StatisticsSecond EditionJohn VerzaniCUNY/College of Staten Island, New York, USA

Chapman & Hall/CRC The R Series

Contents:

DATA. What Is Data? Some R Essentials. AccessingData by Using Indices. Reading in Other Sources ofData. UNIVARIATE DATA. Categorical Data.Numeric Data. Shape of a Distribution. BIVARIATEDATA. Pairs of Categorical Variables. ComparingIndependent Samples. Relationships in NumericData. Simple Linear Regression. MULTIVARIATEDATA. Viewing Multivariate Data. R Basics: DataFrames and Lists. Using Model Formula withMultivariate Data. Lattice Graphics. Types of Data inR. DESCRIBING POPULATIONS. Populations.Families of Distributions. The Central Limit Theorem.SIMULATION. The Normal Approximation for theBinomial for loops. Simulations Related to theCentral Limit Theorem. Defining a Function.Investigating Distributions. Bootstrap Samples.Alternates to for loops. CONFIDENCE INTERVALS.Confidence Interval Ideas. Confidence Intervals for aPopulation Proportion, p. Confidence Intervals forthe Population Mean, µ. Other Confidence Intervals.Confidence Intervals for Differences. ConfidenceIntervals for the Median. SIGNIFICANCE TESTS.Significance Test for a Population Proportion.Significance Test for the Mean (t-Tests). SignificanceTests and Confidence Intervals. Significance Tests forthe Median. Two-Sample Tests of Proportion. Two-Sample Tests of Center. GOODNESS OF FIT. TheChi-Squared Goodness-of-Fit Test. The Chi-SquaredTest of Independence. Goodness-of-Fit Tests forContinuous Distributions. LINEAR REGRESSION.The Simple Linear Regression Model. StatisticalInference for Simple Linear Regression. MultipleLinear Regression. ANALYSIS OF VARIANCE. One-Way ANOVA. Using lm() for ANOVA. ANCOVA. Two-Way ANOVA. TWO EXTENSIONS OF THE LINEARMODEL. Logistic Regression. Nonlinear Models.Appendices. Index

Catalog no. K20484, June 2014, 518 pp.ISBN: 978-1-4665-9073-1, $59.95 / £38.99Also available as an eBook

This bestseller guides students through the basics of R,helping them overcome a steep learning curve. Theauthor does this by breaking the material down intosmall, task-oriented steps. The second edition main-tains the features that made the first edition so popu-lar while updating data, examples, and changes to R.

New to the Second Edition:

• Increased emphasis on more idiomatic R thatprovides a grounding in the functionality of base R

• Discussions on RStudio that help new R usersavoid as many pitfalls as possible

• Use of knitr package, which makes code easierto read and therefore easier to reason about

• Additional information on computer-intensiveapproaches

• Updated examples and data

The book has an accompanying package, UsingR,available from CRAN. The package contains the datasets mentioned in the text, answers to selected prob-lems, a few demonstrations, errata, and sample codefrom the text.

The topics of this text line up closely with traditionalteaching progression; however, the book also high-lights computer-intensive approaches to motivate themore traditional approach. The author emphasizesrealistic data and examples and relies on visualizationtechniques to gather insight. He introduces statisticsand R seamlessly, giving students the tools they needto use R and the information they need to navigatethe sometimes-complex world of statistical comput-ing.Solutions manual, lecture slides, and figure slides availableupon qualifying course adoption

http://www.crcpress.com/product/isbn/9781466590731

4 1-800-634-7064 • 1-859-727-5000 • +44 (0) 1235 400 524 • [email protected]

Introductory Statistics

The R StudentCompanionBrian DennisUniversity of Idaho, Moscow, USA

“An R book for high school-ers! This is an excellent idea,and the quality of the prod-uct is equally excellent. Itmay be suitable for non-cal-culus-based introductorycourses at the college levelas well. … Dennis does agood job dispelling the ‘steep learning curve’ mythconcerning R … . The writing style is clear and live-ly, and the examples should appeal to high schoolstudents. It is high time that introductory statisticsbe taught in an engaging manner that reflects ourown enthusiasm for the subject, with meaningfuldata sets, attractive graphics, and so on. Dennis’book is a fine contribution toward that goal.”

—Journal of Statistical Software, February 2013

• Illustrates how to calculate and graph examplesin R for college science and mathematics courses

• Provides fully developed exercises based aroundthe main precalculus analysis skills needed in thestandard college general education courses inscience and math

• Presents applications drawn from all sciencesand social sciences

• Includes the most often used features of R on areference card in the back of the book

• Contains R exercises that can be performedcooperatively in groups or alone

Selected Contents:

Introduction: Getting Started with R. R Scripts.Functions. Basic Graphs. Data Input and Output.Loops. Logic and Control. Quadratic Functions.Trigonometric Functions. Exponential andLogarithmic Functions. Matrix Arithmetic. Systems ofLinear Equations. Advanced Graphs. Probability andSimulation. Fitting Models to Data. Conclusion—ItDoesn’t Take a Rocket Scientist. Appendix A:Installing R. Appendix B: Getting Help. Appendix C:Common R Expressions. Index.

Catalog no. K13498, September 2012, 360 pp.Soft CoverISBN: 978-1-4398-7540-7, $41.95 / £26.99

Essentials ofMultivariateData AnalysisNeil H. SpencerUniversity of HertfordshireBusiness School, de HavillandCampus, Hatfield, UK

Unlike most books on multi-variate methods, this onemakes straightforward analy-ses easy to perform for stu-dents who are unfamiliarwith advanced mathematical formulae. It uses an eas-ily understood dataset to help explain the techniquesand an Excel add-in to enable basic analyses, withboth available on the book’s CRC Press web page.

Selected Contents:

Frequently Asked Questions. Graphical Presentationof Multivariate Data. Multivariate Tests ofSignificance. Factor Analysis. Cluster Analysis.Discriminant Analysis. Multidimensional Scaling.Correspondence Analysis. References. Index.

Catalog no. K19058, December 2013, 186 pp.Soft CoverISBN: 978-1-4665-8478-5, $59.95 / £34.99Also available as an eBook

Introduction toProbability withTexas Hold’emExamplesFrederic Paik SchoenbergUniversity of California, LosAngeles, USA

“… as a teacher, this is def-initely a book I would rec-ommend as a pleasantintroduction to the world ofprobability theory.”

—CHANCE, June 2013

“… a refreshing new introduction to the subjectmatter. It is certainly worth considering for your nextyear’s intake of students."

—International Statistical Review, 2013

“… it is the laser-like focus of the examples and exer-cises that sets this book apart from other probabili-ty textbooks at this level. … The book is incrediblywell researched …”

—MAA Reviews, February 2012

Catalog no. K11367, December 2011, 199 pp.Soft CoverISBN: 978-1-4398-2768-0, $52.95 / £33.99Also available as an eBook




5

Statistical Theory & Methods


Bestseller!

Bayesian DataAnalysisThird EditionAndrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin

Series: Chapman & Hall/CRCTexts in Statistical Science

“The second edition was reviewed in the September2004 issue of JASA and we now stand ten years laterwith an even more impressive textbook … truly whatBayesian data analysis should be. … this being athird edition begets the question … what’s new(when compared with the second edition)? Quite alot … overall this is truly the reference book for agraduate course on Bayesian statistics and not onlyBayesian data analysis.”

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

Now in its third edition, this classic book continues totake an applied approach to analysis using up-to-dateBayesian methods. Along with new and revised soft-ware code, this edition includes four new chapters onnonparametric modeling, updates the discussion ofcross-validation and predictive information criteria,and improves convergence monitoring and effectivesample size calculations for iterative simulation. It alsocovers weakly informative priors, boundary-avoidingpriors, Hamiltonian Monte Carlo, variational Bayes,and expectation propagation.

• Presents an accessible introduction to Bayesianstatistics

• Focuses on the use of Bayesian inference in practice, with many examples of real statisticalanalyses throughout

• Includes plenty of exercises and bibliographicnotes at the end of each chapter

• Provides data sets, solutions to selected exercises, and other material online

Selected Contents:

Fundamentals of Bayesian Inference. Fundamentalsof Bayesian Data Analysis. Advanced Computation.Regression Models. Nonlinear and NonparametricModels. Appendices.

Catalog no. K11900, November 2013, 675 pp.ISBN: 978-1-4398-4095-5, $69.95 / £44.99Also available as an eBook

Risk Assessmentand DecisionAnalysis withBayesianNetworksNorman Fenton andMartin NeilQueen Mary University ofLondon, UK

“By offering many attrac-tive examples of Bayesiannetworks and by making use of software that allowsone to play with the networks, readers will definite-ly get a feel for what can be done with Bayesian net-works. … the power and also uniqueness of the bookstem from the fact that it is essentially practice ori-ented, but with a clear aim of equipping the devel-oper of Bayesian networks with a clear understand-ing of the underlying theory. Anyone involved ineveryday decision making looking for a better foun-dation of what is now mainly based on intuition willlearn something from the book.”

—Journal of Statistical Theory and Practice, March 2014

“… although there have been several excellentbooks dedicated to Bayesian networks and relatedmethods, these books tend to be aimed at readerswho already have a high level of mathematicalsophistication … This book is an exciting develop-ment because it addresses this problem.”

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

• Focuses on applications and practical modelbuilding using AgenaRisk, a powerful commercial software tool

• Includes real examples from finance, softwareand systems, defense, and the law

• Introduces the necessary probability and statistics where required

Selected Contents:

There Is More to Assessing Risk Than Statistics. TheNeed for Causal, Explanatory Models in RiskAssessment. Measuring Uncertainty: The Inevitabilityof Subjectivity. The Basics of Probability. Bayes’Theorem and Conditional Probability. From Bayes’Theorem to Bayesian Networks. Defining theStructure of Bayesian Networks. Building andEliciting Node Probability Tables. Numeric Variablesand Continuous. Hypothesis Testing and ConfidenceIntervals. Modeling Operational Risk. SystemsReliability Modeling. Bayes and the Law. Appendices.

Catalog no. K10450, November 2012, 524 pp.ISBN: 978-1-4398-0910-5, $83.95 / £43.99



6 1-800-634-7064 • 1-859-727-5000 • +44 (0) 1235 400 524 • [email protected]


An Introductionto GeneralizedLinear ModelsThird EditionAnnette J. DobsonUniversity of Queensland,Herston, Australia

Adrian BarnettQueensland University ofTechnology, Kelvin Grove,Australia

“… explanations are fundamentally sound andaimed well at an upper-level undergrad or earlygraduate student in a statistics-related field. This isa very worthwhile book: a good class text and apractical reference for applied statisticians.”

—Biometrics

Selected Contents:

Model Fitting. Exponential Family and GeneralizedLinear Models. Estimation. Inference. Normal LinearModels. Binary Variables and Logistic Regression.Nominal and Ordinal Logistic Regression. PoissonRegression and Log-Linear Models. ...

Catalog no. C9500, May 2008, 320 pp., Soft CoverISBN: 978-1-58488-950-2, $71.95 / £43.99Also available as an eBook

UnderstandingAdvancedStatisticalMethodsPeter WestfallTexas Tech University, Lubbock,USA

Kevin S.S. HenningSam Houston State University,Huntsville, Texas, USA


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

—Journal of Applied Statistics, 2014

Solutions manual available upon qualifying course adoption

Catalog no. K14873, April 2013, 569 pp.ISBN: 978-1-4665-1210-8, $79.95 / £44.99Also available as an eBook

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

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


Selected Contents:

THE BIG PICTURE: Modeling Basics. Design Matters.Setting the Stage. ESTIMATION AND INFERENCEESSENTIALS: Estimation. Inference, Part I: ModelEffects. Inference, Part II: Covariance Components.WORKING WITH GLMMs: Treatment andExplanatory Variable Structure. Multilevel Models.Best Linear Unbiased Prediction. Rates andProportions. Counts. Time-to-Event Data.Multinomial Data. Correlated Errors ...

Catalog no. K10775, September 2012, 555 pp.ISBN: 978-1-4398-1512-0, $93.95 / £59.99Also available as an eBook

PracticalMultivariateAnalysisFifth EditionAbdelmonem Afifi,Susanne May, andVirginia A. Clark


“I found the text enjoyableand easy to read. Theauthors provide a sufficient description of all themethodology for practical use. Each chapterincludes at least one real-world dataset analysis andthe software commands summary tables included atthe end of every chapter should be particularly help-ful to a practitioner of statistics.”

—Journal of Biopharmaceutical Statistics, 2012

Solutions manual available for qualifying instructors

Selected Contents:

Preparation for Analysis. Applied Regression Analysis.Multivariate Analysis. Appendix. References. Index.

Catalog no. K10864, July 2011, 537 pp.ISBN: 978-1-4398-1680-6, $98.95 / £48.99Also available as an eBook





7



StatisticalMethods forHandlingIncomplete DataJae Kwang Kim and Jun Shao

Along with many examples,this text covers up-to-datestatistical theories and com-putational methods for ana-lyzing incomplete data. Itpresents a thorough treat-ment of statistical theories of likelihood-based infer-ence with missing data. It also discusses numerouscomputational techniques and theories on imputa-tion. Some of the research ideas introduced can bedeveloped further for specific applications.

Selected Contents:

Introduction. Likelihood-Based Approach.Computation. Imputation. Propensity ScoringApproach. Nonignorable Missing Data. Longitudinaland Clustered Data. Application to Survey Sampling.Statistical Matching. Bibliography. Index.


StatisticalTheoryA ConciseIntroductionFelix AbramovichTel Aviv University, Israel

Ya'acov RitovThe Hebrew University ofJerusalem, Israel


“As teachers of theoretical statistics, we can use anew approach, which this text offers. … a helpfulresource for teachers of mathematical statistics whoare looking for an outline of teaching material anduseable depth. Their material attains a workable syl-labus, which can be easily augmented with theteacher’s preferred emphasis. This volume will makea solid contribution to any theoretical statisticsinstructor’s collection due to its convenient size, itsscope of coverage, judicious use of examples, andclarity of exposition.”

—The American Statistician, May 2014


Bayesian Ideasand DataAnalysisAn Introduction forScientists andStatisticiansRonald Christensen,Wesley Johnson, Adam Branscum, andTimothy E Hanson


“Firstly, it provides an intermediate-level course instatistics … given to engineers and scientists requir-ing substantial statistical analysis, as well as mate-rial for a course in Bayesian statistics that is typical-ly offered to statistics students. Secondly, it showshow to perform the analyses by using WinBUGSthroughout the text. I would use this book as a basisfor a course on Bayesian statistics. It is an excellenttext for individual study, and students will find it avaluable reference later in their careers.”—Journal of the Royal Statistical Society: Series A, October 2011


RichlyParameterizedLinear ModelsAdditive, TimeSeries, and SpatialModels UsingRandom EffectsJames S. HodgesUniversity of Minnesota,Minneapolis, USA


“This book is a masterpiece, destined to become aclassic. … There is not presently a unified theory, likethat for linear regression, to explain how, why, andwhen our numerical routines give results that shouldbe questioned, or at least examined further. Even so,this book does the best job I have seen of explainingwhat can go wrong and what the state of the art is.… I am excited by the prospect of teaching a coursefrom this book. Its clarity of thought and presenta-tion are exemplary. I recommend it for anyone whofits complicated models.”

—Michael Lavine, University of Massachusetts Amherst






8 1-800-634-7064 • 1-859-727-5000 • +44 (0) 1235 400 524 • [email protected]


NonparametricMethods inStatistics withSAS ApplicationsOlga KorostelevaCalifornia State University, LongBeach, USA

This classroom-tested bookteaches students how toapply nonparametric tech-niques to statistical data.Along with exercises in eachchapter, the text includes various examples from psy-chology, education, clinical trials, and other areas.Complete SAS codes for all examples are given in thetext. Large data sets for the exercises are available onthe author’s website. A solutions manual is availableupon qualifying course adoption.

Selected Contents:

Hypotheses Testing for Two Samples. HypothesesTesting for Several Samples. Tests for CategoricalData. Nonparametric Regression. NonparametricGeneralized Additive Regression. Time-to-EventAnalysis. Univariate Probability Density Estimation.Resampling Methods for Interval Estimation.

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

AppliedCategorical andCount DataAnalysisWan Tang, Hua He, andXin M. TuUniversity of Rochester, NewYork, USA

“Exercises, combined withpractical data analyses, willcertainly facilitate theadoption of the material.”


“The combination of more advanced and mathe-matical explanations, newer topics, and samplecode from all major software platforms makes thisbook a valuable addition to the literature on cate-gorical data analysis.”

—Journal of the American Statistical Association, September2013

Selected Contents:

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


StochasticModeling andMathematicalStatisticsA Text forStatisticians andQuantitativeScientistsFrancisco J. SamaniegoUniversity of California, Davis, USA

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

Selected Contents:

The Calculus of Probability. Discrete ProbabilityModels. Continuous Probability Models. MultivariateModels. Limit Theorems and Related Topics.Statistical Estimation: Fixed Sample Size Theory.Statistical Estimation: Asymptotic Theory. IntervalEstimation. The Bayesian Approach to Estimation. ...

Catalog no. K15895, January 2014, 622 pp.ISBN: 978-1-4665-6046-8, $89.95 / £57.99Also available as an eBook

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

This book helps studentsobtain an in-depth under-standing of statistical theoryby working on and reviewingsolutions to interesting and challenging exercises ofpractical importance. Unlike similar books, this oneincorporates many exercises that apply to real-worldsettings and provides much more thorough solutions.Instructors can use the material as classroom examples,homework problems, or examination questions. Bymastering the theoretical statistical strategies necessaryto solve the exercises, students will be prepared tostudy even higher-level statistical theory. A solutionsmanual is available upon qualifying course adoption.

Catalog no. K16626, June 2013, 388 pp., Soft CoverISBN: 978-1-4665-7289-8, $59.95 / £38.99Also available as an eBook





9



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

“… one of the best booksavailable for a graduate (oradvanced undergraduate) text for a theory courseon nonparametric statistics. …”

—Biometrics, September 2011

“The book is undoubtedly well written and presentsa good balance of theory and applications. It is suit-able for teaching as well as self-learning. There areexercises in each chapter, which will be helpful inteaching a course. … I would strongly recommendthis book to university libraries, teachers, and under-graduate students … .”

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

Catalog no. C7619, July 2010, 650 pp.ISBN: 978-1-4200-7761-2, $104.95 / £69.99Also available as an eBook

New!

Linear Algebraand MatrixAnalysis forStatisticsSudipto BanerjeeUniversity of Minnesota, Schoolof Public Health, Minneapolis,USA

Anindya RoyUniversity of Maryland BaltimoreCounty, 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 very goodpace. The book is compactly written and mathemat-ically rigorous, yet the style is lively as well as engag-ing. This elegant, sophisticated work will serveupper-level and graduate statistics education well.All and all a book I wish I could have written.”

—Jim Zidek, University of British Columbia


StationaryStochasticProcesses forScientists andEngineersGeorg Lindgren, Holger Rootzen, andMaria Sandsten

“… superbly motivated andillustrated through a wealthof convincing applicationsin science and engineering. It offers a clear guide tothe formulation and mathematical properties ofthese processes and to some non-stationary process-es too, without going too deeply into the mathe-matical foundations … An outstanding text.”

—Clive Anderson, University of Sheffield

Selected Contents:

Stochastic Processes. Stationary Processes. ThePoisson Process and Its Relatives. SpectralRepresentations. Gaussian Processes. Linear Filters—General Theory. AR, MA, and ARMA Models. LinearFilters—Applications. Frequency Analysis and SpectralEstimation. Appendices. References. Index.

Catalog no. K20279, October 2013, 330 pp.ISBN: 978-1-4665-8618-5, $79.95 / £49.99Also available as an eBook

New!

Introduction toMultivariateAnalysisLinear and NonlinearModelingSadanori KonishiChuo University, Tokyo, Japan


This text shows studentshow to use multivariate analysis to extract useful infor-mation from multivariate data and understand thestructure of random phenomena. Along with thebasic concepts of various procedures in traditionalmultivariate analysis, the book covers nonlinear tech-niques for clarifying phenomena behind observedmultivariate data. It primarily focuses on regressionmodeling, classification, discrimination, dimensionreduction, and clustering. Many examples and figuresthroughout facilitate a deep understanding of themultivariate analysis techniques, including how toselect the optimal model.






10 1-800-634-7064 • 1-859-727-5000 • +44 (0) 1235 400 524 • [email protected]


New!

Linear Modelswith RSecond EditionJulian J. FarawayUniversity of Bath, UK


Like its widely praised, best-selling predecessor, this sec-ond edition explains how touse linear models in physical science, engineering,social science, and business applications. The materialon interpreting linear models now distinguishes themain applications of prediction and explanation andintroduces elementary notions of causality. This edi-tion also covers QR decomposition, splines, additivemodels, Lasso, multiple imputation, and false discov-ery rates. It extensively uses R’s ggplot2 graphics pack-age in addition to base graphics.

• Combines statistics and R to seamlessly give acoherent exposition of the practice of linearmodeling

• Offers up-to-date insight on essential data analysis topics, from estimation, inference, andprediction to missing data, factorial models, andblock designs

• Demonstrates the flexibility of linear models inmany examples

• Assumes basic knowledge of R and statistics

• Emphasizes intuition over rigorous proofs

• Presents exercises at the end of each chapter

• Includes datasets and R commands

Selected Contents:

Introduction. Estimation. Inference. Prediction.Explanation. Diagnostics. Problems with thePredictors. Problems with the Error. Transformation.Model Selection. Shrinkage Methods. InsuranceRedlining—A Complete Example. Missing Data.Categorical Predictors. One Factor Models. Modelswith Several Factors. Experiments with Blocks.Appendix: About R. Bibliography. Index.


New!

Introduction toProbabilityJoseph K. BlitzsteinHarvard University, Cambridge,Massachusetts, USA

Jessica HwangStanford University, California,USA


Assuming one semester of calculus, this textbookintroduces probability to undergraduate studentswho want to learn statistics. It clearly explains theimportance of widely used distributions in statistics,such as normal, binomial, and Poisson, and exploreshow they are all connected. The book makes the dis-tributions easier to remember, understand, and workwith by illustrating natural applications where theyarise, including applications of MCMC. R is used toperform statistical calculations.

• Presents definitions, theorems, and proofsthrough stories that preserve mathematical precision and generality

• Focuses on real-world relevance and statisticalthinking

• Includes interesting modern applications, such as Google PageRank, legal and medicalexamples, and applications of MCMC to ecologyand cryptography

• Explains and connects the most important distributions used in statistics

• Contains nearly 600 exercises that reinforce students’ understanding of the material insteadof requiring repetitive calculations

• Supplements key concepts with memorable diagrams

• Explains how to run simulations, make visualiza-tions, and perform statistical calculations using R

Selected Contents:

Probability and Counting. Conditional Probability.Random Variables and Their Distributions.Expectation. Continuous Random Variables.Moments. Joint Distributions. Transformations.Conditional Expectation. Inequalities and LimitTheorems. Markov Chains. Markov Chain MonteCarlo. Poisson Processes. Math. R. Table ofDistributions. Bibliography. Index.

Catalog no. K16714, August 2014, c. 596 pp.Pack - Book and EbookISBN: 978-1-4665-7557-8, $99.95 / £49.99Also available as an eBook



11



New!

Analysis ofCategorical Datawith RChristopher R. BilderUniversity of Nebraska–Lincoln,USA

Thomas M. LoughinSimon Fraser University, Surrey,British Columbia, Canada

“This book gives users thefull scoop when it comes toanalyzing categorical data of all types, and it doesso in an easy-to-understand way, giving confidenceto the reader to go ahead and apply the ideas inpractice. … Through the special attention paid toteaching the basics of R, as well as providing step-by-step particulars in using R in each separateanalysis, Bilder and Loughin help establish and pro-mote a group of confident, comfortable users of thissoftware that can seem a mystery to many. I highlyand happily recommend this book to anyone whoplans to analyze categorical data in their careers—which includes most all of us!”

—Deborah J. Rumsey, The Ohio State University

• Provides descriptions and motivations of theanalysis methods as well as worked exampleswith R code

• Highlights applications in a wide range of disciplines, including medicine, psychology,sports, and ecology

• Uses R not only as a data analysis method butalso as a learning tool

• Discusses solutions to problems frequently mishandled in practice, such as how to incorporate diagnostic testing error into ananalysis and how to analyze data from a complex survey sampling design

• Includes an introduction to R for inexperiencedusers

• Presents an extensive set of exercises at the endof each chapter

• Offers data sets, R programs, and videos on thebook’s website


Selected Contents:

Analyzing a Binary Response, Part 1: Introduction.Analyzing a Binary Response, Part 2: RegressionModels. Analyzing a Multicategory Response.Analyzing a Count Response. Model Selection andEvaluation. Additional Topics. Appendices.Bibliography. Index.

Catalog no. K12597, August 2014, c. 547 pp.ISBN: 978-1-4398-5567-6, $89.95 / £49.99Also available as an eBook

New!

StatisticalInferenceAn IntegratedApproach, Second EditionHelio S. Migon, Dani Gamerman, andFrancisco Louzada


This text presents a balanced account of the Bayesianand frequentist approaches to statistical inference.Along with more examples and exercises, this secondedition includes new material on empirical Bayes andpenalized likelihoods and their impact on regressionmodels and offers expanded material on hypothesistesting, method of moments, bias correction, andhierarchical models. It also compares the Bayesian andfrequentist schools of thought and explores proce-dures that lie on the border between the two.


Coming soon!

NonparametricStatisticalMethods UsingRJohn KlokeUniversity of Pittsburgh,Pennsylvania, USA

Joseph McKeanWestern Michigan University,Kalamazoo, USA

Chapman & Hall/CRC The R Series

Focusing on robust rank-based nonparametric meth-ods, this book covers rank-based fitting and testing formodels ranging from simple location models to gen-eral linear models for uncorrelated and correlatedresponses. Illustrated with real data examples using R,each chapter includes a short problem set with datasets. The corresponding example codes are availableonline. Accessible to nonspecialists, the book alsooffers an appendix with the technical details of thegeometry of rank-based estimation.

Catalog no. K13406, October 2014, c. 288 pp.ISBN: 978-1-4398-7343-4, $79.95 / £49.99Also available as an eBook




12 1-800-634-7064 • 1-859-727-5000 • +44 (0) 1235 400 524 • [email protected]

Computational Statistics

A GentleIntroduction toStataRevised Third EditionAlan C. AcockOregon State University,Corvallis, USA

Updated to reflect the newfeatures of Stata 11, thisthird edition continues tohelp new Stata usersbecome proficient in Stata.After reading this introductory text, students will beable to enter, build, and manage a data set as well asperform fundamental statistical analyses. This editionincludes a new chapter on the analysis of missing dataand the use of multiple-imputation methods. It alsoprovides an extensive revision of the chapter onANOVA, along with additional material on the appli-cation of power analysis. Each chapter includes exer-cises and real data sets are used throughout.

• Reflects the features of Stata 11

• Shows how to enter, build, and manage a dataset

• Supplements basic statistical modeling topicswith discussions of effect sizes and standardizedcoefficients

• Discusses various model selection criteria, suchas semipartial correlations

• Employs real data sets, such as the GeneralSocial Surveys from 2002 and 2006

Selected Contents:

Support Materials. Getting Started. Entering Data.Preparing Data for Analysis. Working withCommands, Do-Files, and Results. DescriptiveStatistics and Graphs for One Variable. Statistics andGraphs for Two Categorical Variables. Tests for Oneor Two Means. Bivariate Correlation and Regression.Analysis of Variance. Multiple Regression. LogisticRegression. Measurement, Reliability, and Validity.Working with Missing Values—Multiple Imputation.Appendix. References. Author Index. Subject Index.

Catalog no. N10594, March 2012, 401 pp.Soft CoverISBN: 978-1-59718-109-9, $79.95 / £49.99

The BUGS BookA PracticalIntroduction toBayesian AnalysisDavid Lunn, Chris Jackson, Nicky Best,Andrew Thomas, andDavid Spiegelhalter


“… highly relevant not onlyfor beginners but for advanced users as well. … anotable addition to the growing range of introduc-tory Bayesian textbooks that have been publishedwithin the last decade. It is unique in its focus onexplicating state-of-the-art computational Bayesianstrategies in the WinBUGS software. … The BUGSBook will become a classic Bayesian textbook andprovide invaluable guidance to practicing statisti-cians, academics, and students alike.”

—Journal of Biopharmaceutical Statistics, 2014

Catalog no. C8490, October 2012, 399 pp.Soft CoverISBN: 978-1-58488-849-9, $52.95 / £25.99Also available as an eBook

Probability andStatistics forComputerScientistsSecond EditionMichael BaronUniversity of Texas at Dallas,Richardson, USA

“It could work well as arequired text for anadvanced undergraduate orgraduate course.”

—Computing Reviews, January 2014


Selected Contents:

Introduction and Overview. Probability andRandom Variables: Probability. Discrete RandomVariables and Their Distributions. ContinuousDistributions. Computer Simulations and MonteCarlo Methods. Stochastic Processes: StochasticProcesses. Queuing Systems. Statistics: Introductionto Statistics. Statistical Inference I. Statistical InferenceII. Regression. Appendix. Index.





13

Computational Statistics


StatisticalComputing with RMaria L. RizzoBowling Green State University,Ohio, USA

Chapman & Hall/CRC The RSeries

“… an excellent tutorial onthe R language, providingexamples that illustrate pro-gramming concepts in thecontext of practical computational problems. Thebook will be of great interest for all specialists work-ing on computational statistics and Monte Carlomethods for modeling and simulation.”

—Zentralblatt Math, 2008

This text covers the traditional core material of com-putational statistics, with an emphasis on using the Rlanguage via an examples-based approach. An excel-lent tutorial on R programming techniques used inpractical computational problems, it illustrates everyalgorithm with at least one fully implemented exam-ple coded in R. Key topics covered include the simu-lation of random variables from probability distribu-tions, the visualization of multivariate data, MonteCarlo integration and variance reduction methods,Monte Carlo methods in inference, bootstrap andjackknife, MCMC methods, and density estimation.

• Provides a tutorial on R programming tech-niques used in practical computational problems

• Covers the most important topics in computa-tional statistics, including Monte Carlo methods,bootstrap, MCMC, and the visualization of mul-tivariate data

• Illustrates every algorithm with at least one fullyimplemented example coded in R

• Includes numerous exercises and offers thesource code for all examples online


Selected Contents:

Probability and Statistics Review. Methods forGenerating Random Variables. Visualization ofMultivariate Data. Monte Carlo Integration andVariance Reduction. Monte Carlo Methods inInference. Bootstrap and Jackknife. PermutationTests. Markov Chain Monte Carlo Methods.Probability Density Estimation. Numerical Methodsin R. Appendices. References. Index.

Catalog no. C5459, November 2007, 416 pp.ISBN: 978-1-58488-545-0, $99.95 / £45.99

StatisticalComputing inC++ and RRandall L. EubankArizona State University, Tempe,USA

Ana KupresaninLawrence Livermore NationalLaboratory, California, USA

“… the first treatment ofparallel programming in Rthat I have seen in a book.The text is replete with code examples and there arenumerous end-of-chapter exercises.”


Selected Contents:

Computer Representation of Numbers. A Sketch ofC++. Generation of Pseudo-Random Numbers.Programming in R. Creating Classes and Methods inR. Numerical Linear Algebra. NumericalOptimization. Abstract Data Structures. DataStructures in C++. Parallel Computing in C++ and R.An Introduction to Unix. An Introduction to R. C++Library Extensions (TR1). The Matrix and VectorClasses. The ranGen Class.

Catalog no. C6650, December 2011, 556 pp.ISBN: 978-1-4200-6650-0, $93.95 / £62.99Also available as an eBook

Foundations ofStatisticalAlgorithmsWith References to RPackagesClaus Weihs, Olaf Mersmann, and Uwe LiggesTU Dortmund University,Germany

This text emphasizes recur-ring themes in all statisticalalgorithms, including computation, assessment andverification, iteration, intuition, randomness, repetitionand parallelization, and scalability. It touches on topicsnot usually covered in similar books, namely, systemat-ic verification and the scaling of many established tech-niques to very large databases. Each chapter includesexamples, exercises, and selected solutions.

Selected Contents:

Introduction. Computation. Verification. Iteration.Deduction of Theoretical Properties. Randomization.Repetition. Scalability and Parallelization.Bibliography. Index.

Catalog no. K13688, December 2013, 500 pp.ISBN: 978-1-4398-7885-9, $79.95 / £38.99




14 1-800-634-7064 • 1-859-727-5000 • +44 (0) 1235 400 524 • [email protected]

Biostatistics & Epidemiology

EpidemiologyStudy Design andData Analysis, Third EditionMark WoodwardUniversity of Oxford, UK;University of Sydney, Australia;and Johns Hopkins University,Baltimore, Maryland, USA


Updated and expanded, thispopular text shows students how statistical principlesand techniques can help solve epidemiological prob-lems. Along with more exercises and examples usingboth Stata and SAS, this third edition includes a newchapter on risk scores and clinical decision rules, anew chapter on computer-intensive methods, andnew sections on binomial regression models, compet-ing risk, information criteria, propensity scoring, andsplines. Supporting materials are available on thebook’s CRC Press web page and a solutions manual isavailable upon qualifying course adoption.

Catalog no. K11828, December 2013, 898 pp.ISBN: 978-1-4398-3970-6, $99.95 / £49.99Also available as an eBook

RegressionModels as a Tool inMedicalResearchWerner VachInstitute of Medical Biometry andMedical Informatics, Freiburg,Germany

“… a very helpful contribution, especially forresearchers in medical sciences when performingtheir statistical analyses and trying to interpret theresults obtained. … This book provides plenty ofpractical knowledge about these basic models andalso some of their extensions that is often not easyto find from statistical textbooks or from softwaremanuals. The basic methods are well explained andillustrated by numerous practical examples, mainlyusing simulated datasets.”



MedicalBiostatisticsThird EditionAbhaya Indrayan

Chapman & Hall/CRCBiostatistics Series

“The third edition ofMedical Biostatistics pres-ents an almost complete ref-erence for medical and bio-statistics professionals, cov-ering many topics in introductory and intermediatebiostatistics. … a good resource for an undergradu-ate course in biostatistics or related fields. … Thestrengths of the book are the examples usedthroughout and the comprehensive coverage interms of number of topics … a great reference for aresearcher in the medical or biostatistics field who isnot concerned about mathematical derivations.”—Journal of the American Statistical Association, March 2014

Catalog no. K13952, August 2012, 1024 pp.ISBN: 978-1-4398-8414-0, $135.95 / £86.00Also available as an eBook

MultivariateSurvival Analysisand CompetingRisksMartin J. CrowderImperial College, University ofLondon, UK


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:Multivariate Data and Distributions. Frailty andCopulas. 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.






15

Statistics for Engineering & Physical Science


ProbabilityFoundations forEngineersJoel A. NachlasVirginia Polytechnic Institute andState University, Blacksburg, USA

“… an excellent introducto-ry book on probability forengineers and it will pre-pare the IE, CE, and EE stu-dents for advanced coursesthat deal with randomprocesses.”

—Edward A. Pohl, University of Arkansas

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

—Uday Kumar, Luleå University of Technology

“… this book takes a fresh approach to teachingundergraduate engineering students the fundamen-tals of probability. The book exploits students’ exist-ing intuition regarding probabilistic concepts whenpresenting these concepts in a more rigorous man-ner. Students should be better able to retain theknowledge gained through reading this text becauseof the relevance of the examples and applications.”

—Lisa Maillart, University of Pittsburgh

Suitable for a first course in probability theory, thistextbook covers theory in an accessible 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.Solutions manual available upon qualifying course adoption

Selected Contents:

Historical Perspectives. A Brief Review of Set Theory.Probability Basics. Random Variables andDistributions. Joint, Marginal, and ConditionalDistributions. Expectation and Functions of RandomVariables. Moment-Generating Functions.Approximations and Limiting Behavior. Appendix:Cumulative Poisson Probabilities. Index.

Catalog no. K14453, May 2012, 184 pp.ISBN: 978-1-4665-0299-4, $129.95 / £82.00Also available as an eBook

Introduction toLinearOptimizationand Extensionswith MATLAB®

Roy H. KwonUniversity of Toronto, Ontario,Canada

Operations Research Series

This book fills the need for anintroductory book on linearprogramming that discusses the important ways tomitigate parameter uncertainty. Presenting basicsbefore theory, the author presents a rigorous devel-opment of linear programming theory and methods.

The book introduces both stochastic programmingand robust optimization as frameworks to deal withparameter uncertainty. This unusual approach—developing these topics in an introductory book—highlights their importance. Since most applicationsrequire decisions to be made in the face of uncertain-ty, the early introduction of these topics facilitatesdecision making in real-world environments.

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

• Illustrates major methods and algorithms usingMATLAB

• Rigorously develops linear programming theoryand methods

• Includes financial optimization case studies andMATLAB® exercises, with the code available onthe book’s CRC Press web page

• Contains an extensive bibliography with sourcesfrom both classical and recent literature


Selected Contents:

FUNDAMENTALS: Geometry of Linear Optimization.Simplex Method. Duality and Sensitivity Analysis.EXTENSIONS: Decomposition in LinearOptimization. Quadratic Optimization. Interior PointMethods. ROBUST STRATEGIES FOR LINEAR OPTI-MIZATION: Stochastic Programming. Robust LinearOptimization.




16 1-800-634-7064 • 1-859-727-5000 • +44 (0) 1235 400 524 • [email protected]

Statistics for Engineering & Physical Science

Introduction toStatisticalProcess ControlPeihua QiuUniversity of Florida, Gainesville,USA


“… an excellent choice asthe primary textbook in anSPC course.”

—Changliang Zou, Nankai University

A major tool for quality control and management, sta-tistical process control (SPC) monitors sequentialprocesses, such as production lines and Internet traf-fic, to ensure that they work stably and satisfactorily.Along with covering traditional methods, this bookdescribes many recent SPC methods that improveupon the more established techniques. The author—a leading researcher on SPC—shows how these meth-ods can handle new applications. Pseudo codes arepresented for important methods and all R functionsand datasets are available on the author’s website.

• Explores the major advantages and limitations oftraditional and state-of-the-art SPC methods

• Offers practical guidelines on implementing thetechniques

• Examines the most recent research results in var-ious areas, including univariate and multivariatenonparametric SPC, SPC based on change-pointdetection, and profile monitoring

• Keeps the mathematical and statistical prerequi-sites to a minimum, only requiring basic linearalgebra, some calculus, and introductory statis-tics

• Provides more advanced or technical material indiscussions at the end of each chapter, alongwith exercises that encourage students to prac-tice with the methods

• Presents pseudo codes for important methods

• Includes all R functions and datasets on theauthor’s website

Selected Contents:

Introduction. Basic Statistical Concepts and Methods.Univariate Shewhart Charts and Process Capability.Univariate CUSUM Charts. Univariate EWMA Charts.Univariate Control Charts by Change-PointDetection. Multivariate Statistical Process Control.Univariate Nonparametric Process Control.Multivariate Nonparametric Process Control. ProfileMonitoring. Appendices. Bibliography. Index.

Catalog no. K12137, October 2013, 520 pp.ISBN: 978-1-4398-4799-2, $89.95 / £57.99Also available as an eBook

ProbabilisticModels forDynamicalSystemsSecond EditionHaym Benaroya, Seon Mi Han, and Mark Nagurka

This self-contained bookintroduces engineering stu-dents to randomness in vari-ables, time-dependent func-tions, and solution methods of the governing equa-tions. After completing the book, students will have amuch better understanding of current research andbe able to participate in advanced design. A solutionsmanual is available upon qualifying course adoption.

Selected Contents:

Applications. Events and Probability. RandomVariable Models. Functions of Random Variables.Random Processes. Single Degree-of-FreedomVibration. Multi Degree-of-Freedom Vibration.Continuous System Vibration. Reliability. Nonlinearand Stochastic Dynamic Models. NonstationaryModels. Monte Carlo Methods. Fluid-InducedVibration. Probabilistic Models in Controls andMechatronic Systems. Index.


New!

BayesianNetworksWith Examples in RMarco ScutariJean-Baptiste DenisUnité de RechercheMathématiques et InformatiqueAppliquées, INRA, Jouy-en-Josas,France

Suitable for graduate stu-dents and non-statisticians,this text introduces Bayesian networks using a hands-on approach with simple yet meaningful examples inR illustrating each step of the modeling process. Thebook explains the whole process of Bayesian networkmodeling, from structure learning to parameter learn-ing to inference. It also gives a concise but rigoroustreatment of the fundamentals of Bayesian networks,offers an introduction to causal Bayesian networks,and evaluates real-world examples involving causalprotein signaling and body composition prediction.





17

Statistics for Business, Finance, & Economics


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

Chapman & Hall/CRCFinancial Mathematics Series

“The depth and breadth ofthis stand-alone textbookon computational methodsin finance is astonishing. Itbrings together a full spectrum of methods withmany practical examples. … an excellent synthesisof numerical methods needed for solving practicalproblems in finance. This book provides plenty ofexercises and realistic case studies. Those who workthrough them will gain a deep understanding of themodern computational methods in finance. … itseems to be an excellent teaching book.”


“… there are several sections on topics that arerarely treated in textbooks: saddle point approxima-tions, numerical solution of PIDEs, and others. Thereis also extensive material on model calibration,including interest rate models and filteringapproaches. The book is a very comprehensive anduseful reference for anyone, even with limited math-ematical background, who wishes to quickly under-stand techniques from computational finance.”

—Zentralblatt MATH 1260

Helping students accurately price a vast array of deriv-atives, this self-contained text explains how to solvecomplex functional equations through numericalmethods. Developed from his courses at ColumbiaUniversity and the Courant Institute of New YorkUniversity, the author covers key computational meth-ods in finance, model calibration and optimization,and techniques for parameter estimation.

Selected Contents:

Pricing and Valuation: Stochastic Processes andRisk-Neutral Pricing. Derivatives Pricing via TransformTechniques. Introduction to Finite Differences.Derivative Pricing via Numerical Solutions of PDEs.Derivative Pricing via Numerical Solutions of PIDEs.Simulation Methods for Derivatives Pricing.Calibration and Estimation: Model Calibration.Filtering and Parameter Estimation. References.Index.


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


“I recommend Erik Schlogl’snew book to all those inter-ested in model implementation. From quasi-randomsequences to HJM to the Excel interface, with fullC++ code, there is something here for everyone.”

—Jim Gatheral, Baruch College, CUNY

“… this book contains a clear and careful discussionof many of the key derivatives pricing modelstogether with object-oriented C++ code. Substantialdiscussion of the design choices made is also includ-ed. I believe that this book is destined to be part ofevery financial engineer’s toolkit.”

—Mark Joshi, University of Melbourne

Catalog no. C4797, November 2013, 354 pp.ISBN: 978-1-58488-479-8, $79.95 / £49.99Also available as an eBook

StochasticProcesses withApplications toFinanceSecond EditionMasaaki KijimaTokyo Metropolitan University,Japan


Selected Contents:

Elementary Calculus: Towards Ito’s Formula.Elements in Probability. Useful Distributions inFinance. Derivative Securities. Change of Measuresand the Pricing of Insurance Products. A Discrete-Time Model for Securities Market. Random Walks.The Binomial Model. A Discrete-Time Model forDefaultable Securities. Markov Chains. Monte CarloSimulation. From Discrete to Continuous: Towardsthe Black-Scholes. Basic Stochastic Processes inContinuous Time. A Continuous-Time Model forSecurities Market. Term-Structure Models andInterest-Rate Derivatives. A Continuous-Time Modelfor Defaultable Securities. References. Index.





18 1-800-634-7064 • 1-859-727-5000 • +44 (0) 1235 400 524 • [email protected]


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


“… a good review of themathematics of option pric-ing. The chapters are well written and were clear tome.”

—INFORMS Journal on Computing, 2013

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

—Mathematical Reviews, December 2013

Developed from the author’s course on Monte Carlosimulation, this text provides a self-contained intro-duction to Monte Carlo methods in financial engi-neering. It covers common variance reduction tech-niques, the cross-entropy method, and the simulationof diffusion process models.

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.

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

• Covers the simulation of diffusion process models

• Assumes minimal background in mathematicsand finance

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

• Includes many hand-and-paper and MATLABcoding exercises at the end of every chapter

Selected Contents:

Review of Probability. Brownian Motion. ArbitrageFree Pricing. Monte Carlo Simulation. GeneratingRandom Variables. Variance Reduction Techniques.Importance Sampling. Stochastic Calculus.Simulation of Diffusions. Sensitivity Analysis.Appendices. Bibliography. Index.


StochasticFinanceAn Introduction withMarket ExamplesNicolas PrivaultNanyang TechnologicalUniversity, Singapore

Selected Contents:

Assets, Portfolios, andArbitrage, Discrete-TimeModel. Pricing and Hedging in Discrete Time.Brownian Motion and Stochastic Calculus. The Black-Scholes PDE. Martingale Approach to Pricing andHedging. Estimation of Volatility. Exotic Options.American Options. Change of Numéraire andForward Measures. Forward Rate Modeling. Pricingof Interest Rate Derivatives. Credit Default. StochasticCalculus for Jump Processes. Pricing and Hedging inJump Models. Basic Numerical Methods. Appendix.Exercise Solutions. References. Index.Solutions manual available upon qualifying course adoption


New!

FinancialMathematicsA ComprehensiveTreatmentGiuseppe Campolieti andRoman N. MakarovWilfrid Laurier University,Waterloo, Ontario, Canada

“As the owner of literallythousands of books on themathematics of arbitrage,I’m sorely tempted to sell my collection and buy thisbook as a replacement. … I commend the authors fortheir authoritative and comprehensive treatment.”

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

“The authors treat the subjects rigorously but withplenty of examples, paying close attention to anaudience that may encounter the subject matter forthe first time, but aware that others will have seen itin different form earlier and may be looking for a dif-ferent angle. This is a book that will find its way intoclassrooms worldwide.”

—Luis Seco, University of Toronto


Catalog no. K14142, March 2014, 829 pp.ISBN: 978-1-4398-9242-8, $89.95 / £57.99Also available as an eBook




19



Coming soon!

Statistics forFinanceErik Lindström, Henrik Madsen, and Jan Nygaard Nielsen


Bridging the gap betweentheoretical books on sto-chastic finance and appliedbooks on financial engineer-ing, this text provides an introduction to statisticalmethods for finance. Designed for mathematics andstatistics students, the book discusses the role that sta-tistics and mathematics play in financial engineering.It covers the necessary mathematical and statisticsbackground and explores security markets, interestrate models, and term structure. Many data examplesillustrate the methods and numerous problems enablethe book to be used as a course text or for self-study.

• Presents background material on mathematicsand statistics, including probability, linear models, stochastic calculus, and stochastic differential equations

• Covers many key topics from financial engineering

• Balances theory and applications with numerousexamples and problems throughout

Selected Contents:

Introduction. Fundamentals. Discrete Time Finance.Linear Time Series Models. Nonlinear Time SeriesModels. Kernel Estimators in Time Series Analysis.Stochastic Calculus. Stochastic Differential Equations.Continuous Time Security Markets. StochasticInterest Rate Models. Discrete Time Approximations.Projections in Hilbert Spaces. Filtering and PredictionTheory. Estimation of Parameters in SDEs. The TermStructure of Interest Rates. Estimation of the TermStructure. Appendices.

Catalog no. K22604, February 2015, c. 320 pp.ISBN: 978-1-4822-2899-1, $89.95 / £57.99Also available as an eBook

Coming soon!

Pricing inGeneralInsurancePietro ParodiWillis LTD., London, UK

This textbook is a practicalintroduction to all aspects ofgeneral insurance pricing.Closely following the syl-labus of the ST8 exam of theUK Actuarial Profession, thebook was developed from the author’s actuarial sci-ence course at the Cass Business School. Many realexamples and case studies illustrate the various topicswhile numerous exercises, some based on past ST8exams, make the book suitable for teaching or self-study.


Coming soon!

MathematicalStatistics forAppliedEconometricsCharles B MossUniversity of Florida, Gainesville,USA

Unlike standard mathemati-cal statistics texts, this onetailors mathematical statis-tics topics and real-worldexamples to economics students. The book givesguidance on computing with Gauss, R, MATLAB®, andMathematica® and provides numerous exercises. Asolutions manual is available upon qualifying courseadoption.

Selected Contents:

DEFINING RANDOM VARIABLES: Introduction toStatistics, Probability and Econometrics. RandomVariables and Probability Distributions. Moments andMoment Generating Functions. Binomial andNormal Random Variables. ESTIMATION: LargeSample Theory. Point Estimation. Interval Estimation.Testing Hypothesis. ECONOMETRIC APPLICA-TIONS: Elements of Matrix Analysis. RegressionApplications in Econometrics.





20 1-800-634-7064 • 1-859-727-5000 • +44 (0) 1235 400 524 • [email protected]

Statistics for Biological Sciences

Foundationaland AppliedStatistics forBiologists UsingRKen A. AhoIdaho State University, Pocatello,USA

Full of biological applica-tions, exercises, and interac-tive graphical examples, thistext presents comprehensivecoverage of both modern analytical methods and sta-tistical foundations. The author harnesses the inherentproperties of the R environment to enable students toexamine the code of complicated procedures step bystep and thus better understand the process ofobtaining analysis results. The graphical capabilities ofR are used to provide interactive demonstrations ofsimple to complex statistical concepts. R code andother materials are available online.

• Covers a wide range of analytical topics, includ-ing bootstrapping, Bayesian MCMC procedures,regression, model selection, GLMs, GAMs, non-linear models, ANOVA, mixed effects models,and permutation approaches

• Emphasizes the understanding of statistical foundations

• Provides R code for all analyses and uses R togenerate the figures

• Includes many biological examples throughoutand extensive exercises at the end of each chapter

• Reviews linear algebra applications and additional mathematical reference material inthe appendix

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

Selected Contents:

FOUNDATIONS: Philosophical and HistoricalFoundations. Introduction to Probability. ProbabilityDensity Functions. Parameters and Statistics. IntervalEstimation: Sampling Distributions, ResamplingDistributions, and Simulation Distributions.Hypothesis Testing. Sampling Design andExperimental Design. APPLICATIONS: Correlation.Regression. ANOVA. Tabular Analyses. Appendix.References. Index.


Coming soon!

Introduction toStatistical DataAnalysis for theLife SciencesSecond EditionClaus Thorn Ekstrøm andHelle SørensenUniversity of Copenhagen,Denmark

Selected Contents:

Description of Samples and Populations. LinearRegression. Comparison of Groups. The NormalDistribution. Statistical Models, Estimation, andConfidence Intervals. Hypothesis Tests. ModelValidation and Prediction. Linear Normal Models.Probabilities. The Binomial Distribution. Analysis ofCount Data. Logistic Regression. Case Exercises.Appendices. Bibliography. Index.Solutions manual available upon qualifying course adoption

Catalog no. K23251, November 2014, c. 504 pp.Soft CoverISBN: 978-1-4822-3893-8, $69.95 / £44.99Also available as an eBook

New!

StatisticalMethods inBiologyDesign and Analysisof Experiments andRegressionS.J. Welham, S.A. Gezan,S.J. Clark, and A. Mead

Selected Contents

Introduction. A Review of Basic Statistics. Principlesfor Designing Experiments. Models for a SingleFactor. Checking Model Assumptions.Transformations of the Response. Models withSimple Blocking Structure. Extracting Informationabout Treatments. Models with Complex BlockingStructure. Replication and Power. Dealing with Non-Orthogonality. Models for a Single Variate: SimpleLinear Regression. Checking Model Fit. Models forSeveral Variates: Multiple Linear Regression. Modelsfor Variates and Factors. Incorporating Structure:Mixed Models. Models for Curved Relationships.Models for Non-Normal Responses: GeneralizedLinear Models. Practical Design and Data Analysis forReal Studies. References. Appendices.





21

Statistics for Biological Sciences


New!

Basic Statistics and Pharmaceutical Statistical ApplicationsThird EditionJames E. De MuthUniversity of Wisconsin–Madison, USA

Pharmacy Education Series

Selected Contents:

INTRODUCTION

PROBABILITY

SAMPLING

PRESENTATION MODES

MEASURES OF CENTRAL TENDENCY

THE NORMAL DISTRIBUTION AND DATA TRANS-FORMATION

CONFIDENCE INTERVALS AND TOLERANCE LIMITS

HYPOTHESIS TESTING

t-TESTS

ONE-WAY ANALYSIS OF VARIANCE (ANOVA)

MULTIPLE COMPARISON TESTS

FACTORIAL DESIGNS: AN INTRODUCTION

CORRELATION

REGRESSION ANALYSIS

Z-TESTS OF PROPORTIONS

CHI SQUARE TESTS

MEASURES OF ASSOCIATION

ODDS RATIOS AND RELATIVE RISK RATIOS

EVIDENCE-BASED PRACTICE: AN INTRODUCTION

SURVIVAL STATISTICS

NONPARAMETRIC TESTS

STATISTICAL TESTS FOR EQUIVALENCE

OUTLIER TESTS

STATISTICAL ERRORS IN THE LITERATURE

APPENDICES

INDEX


Building on its best-selling predecessors, this third edi-tion covers statistical topics most relevant to those inthe pharmaceutical industry and pharmacy practice. Itfocuses on the fundamentals required to understanddescriptive and inferential statistics for problem solv-ing. Incorporating new material in virtually everychapter, this third edition now provides informationon software applications to assist with evaluating data.

New to the Third Edition:

• Use of Excel® and Minitab® for performing statis-tical analysis

• Discussions of nonprobability sampling proce-dures, determining if data is normally distrib-uted, evaluation of covariances, and testing forprecision equivalence

• Expanded sections on regression analysis, chisquare tests, tests for trends with ordinal data,and tests related to survival statistics

• Additional nonparametric procedures, includingthe one-sided sign test, Wilcoxon signed-rankstest, and Mood’s median test

With the help of flow charts and tables, the author dis-pels some of the anxiety associated with using basicstatistical tests in the pharmacy profession and helpsstudents correctly interpret their results using statisti-cal software. Through the text’s problems andworked-out examples, students better understandhow the mathematics works, the logic behind manyof the equations, and the tests’ outcomes.


22 1-800-634-7064 • 1-859-727-5000 • +44 (0) 1235 400 524 • [email protected]

Statistics for Social Science & Psychology

GeneralizedLinear Modelsfor Categoricaland ContinuousLimitedDependentVariablesMichael SmithsonThe Australian NationalUniversity, Canberra

Edgar C. MerkleUniversity of Missouri, Columbia, USA

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

Designed for graduate students in the behavioral,social, health, and medical sciences, this text employsgeneralized linear models, including mixed models,for categorical and limited dependent variables.Categorical variables include both nominal and ordi-nal variables. Discrete or continuous limited depend-ent variables have restricted support, whetherthrough censorship, truncation, or their nature. Thebook incorporates examples of truncated counts, cen-sored continuous variables, and doubly bounded con-tinuous variables, such as percentages.

• Provides extensive coverage of continuous limit-ed dependent variables, including material ondoubly bounded variables

• Presents a thorough and consistent treatment ofover-dispersion and heteroscedasticity, includingtests for them and techniques for modelingthem

• Integrates coverage of “boundary inflation”issues, such as zero inflation in counts and zeroor one inflation in proportions

• Highlights extensions of models to includemixed models and Bayesian MCMC estimation

• Includes worked examples using the R environ-ment, focusing on packages such as VGAM andbetareg

Selected Contents:

Introduction and Overview. DISCRETE VARIABLES:Binary. Nominal Multi-Category. Ordinal-Categorical.Count Data. CONTINUOUS VARIABLES: DoublyBounded. Censored and Truncated. EXTENSIONS:Multi-Level Models. Bayesian MCMC Estimation.Appendices. Web-Based Supplementary Materials.


NonparametricStatistics forSocial andBehavioralSciencesM. Kraska-MIllerAuburn University, Alabama, USA

Incorporating a hands-onpedagogical approach, thistext is the only current non-parametric book writtenspecifically for students in the behavioral and socialsciences. It demonstrates practical applications of themost common nonparametric procedures using IBM’sSPSS software. A solutions manual is available uponqualifying course adoption.

Selected Contents:

Introduction to Research in Social and BehavioralSciences. Introduction to Nonparametric Statistics.Analysis of Data to Determine Association andAgreement. Analyses for Two Independent Samples.Analysis of Multiple Independent Samples. Analysisof Two Dependent Samples. Tests for MultipleRelated Samples. Analysis of Single Samples.


New!

Modern SurveySamplingArijit ChaudhuriIndian Statistical Institute,Kolkata

Selected Contents:

Concepts of Population,Sample, Sampling, Intervaland Point Estimation andPosing the Problem ofSampling. Size of Population, Size of Sample,Sampling Design, Sampling Scheme. UnequalProbability Sampling, Ratio-Estimation, Lahiri’sSampling Scheme, Hartley-Ross Estimator. Stratifiedand Cluster Sampling. Super-Population Modeling,Prediction Approach, Model-Assisted Approach, andBayesian Methods. Randomized Response andIndirect Questioning. Small Area Estimation. NetworkSampling, Adaptive Sampling, Size Control, andControlled Sampling. Analytical Surveys.Bibliography. Index.Solutions manual available upon qualifying course adoption





Premium Science, Technology, and Medical eBook Collections

www.CRCnetBASE.com

• • • • •

10 Million Pages of Rich Content

✦ More than 12,000 references in more than350 subject areas

✦ Authoritative content from trusted experts

✦ Up-to-date information with new titlesadded monthly

Contact the CRCnetBASE team [email protected] • 1-888-318-2367

Ask about Your Free Trial

http://www.crcnetbase.com/

http://www.crcnetbase.com/

mailto:[email protected]

Statistics

Documents

categorical data

numeric data

univariate data

simple linear regression

sample tests of proportion

r basics

basics of r

computational statistics