Statistics Courses

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CRC Press Textbooks

New and Important Textbooks for Your

Statistics Courses

WWW.CRCTEXTBOOKS.COM

CRC Press Taylor & Francis Group

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CONTENTS Introductory Statistics & General References ..........3 Statistical Theory & Methods..................................5 Computational Statistics ......................................10 Biostatistics ............................................................13 Statistical Genetics & Bioinformatics ....................16 Page 7

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Statistics for Engineering & Physical Science ........17 Statistics for Finance..............................................19 Statistics for Biological Sciences ............................21 Statistics for Social Science & Psychology ............22

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Introductory Statistics & General References Introduction to Probability with Texas Hold’em Examples

Introduction to the Theory of Statistical Inference

Frederic Paik Schoenberg

Hannelore Liero

University of CaliforniaLos Angeles, USA

University of Potsdam, Germany

“… it is the laser-like focus of the examples and exercises that sets this book apart from other probability textbooks at this level. … The book is incredibly well-researched—examples are drawn from actual televised poker games, and many explorations of the probabilities in play in a given game situation conclude with a sentence about what really happened, which is a nice touch.”

Uppsala University, Sweden

—Mark Bollman, MAA Reviews, February 2012

• Illustrates both standard and advanced probability topics using real examples of Texas Hold’em hands, rather than the typical balls in urns • Discusses random walks and the arcsine laws • Shows students how to use R to compute/approximate probabilities and simulate Texas Hold’em hands or tournaments

Silvelyn Zwanzig “… it provides in-depth explanations, complete with proofs, of how statistics works. … The book has several user-friendly aspects. One is the use of eight example data sets to illustrate the theory throughout the text. … Another big help are the detailed solutions provided for the problems that appear at the end of each chapter. …

Also helpful: Theoretical or difficult material that can be skipped is marked with an asterisk. … The text analyzes not just methods one learns in a first statistics course, but alternatives as well. …”

—David A. Huckaby, MAA Reviews, February 2012

Catalog no. K12437, July 2011, 284 pp., Soft Cover ISBN: 978-1-4398-5292-7, $72.95 / £31.99 Also available as an eBook

• Covers important poker concepts, such as pot and implied odds, the “fundamental theorem of poker,” and quantification of luck and skill • Presents the results of an optimal strategy for simplified poker games • Includes exercises in each chapter, with selected solutions in an appendix

A Whistle-Stop Tour of Statistics

• Offers a dedicated R package on CRAN that simulates hands and tournaments of Texas Hold’em

Brian S. Everitt

Selected Contents:

Selected Contents:

Probability Basics Counting Problems

Some Basics and Describing Data

Conditional Probability and Independence

Probability

Expected Value and Variance

Estimation

Discrete Random Variables

Inference

Continuous Random Variables

Analysis of Variance Models

Collections of Random Variables

Linear Regression Models

Simulation and Approximation Using Computers

Logistic Regression and the Generalized Linear Model

Appendices

Survival Analysis

References and Suggested Reading

Longitudinal Data and Their Analysis

Professor Emeritus, King’s College, London, UK

Index

Multivariate Data and Their Analysis

Catalog no. K11367, December 2011, 199 pp.

Soft Cover, ISBN: 978-1-4398-2768-0 $49.95 / £31.99

Catalog no. K13590, December 2011, 211 pp.

Soft Cover, ISBN: 978-1-4398-7748-7 $41.95 / £26.99

Also available as an eBook

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

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Introductory Statistics & General References The A-Z of Error-Free Research

The R Student Companion Brian Dennis

Phillip I. Good

University of Idaho, Moscow, USA

Consultant, Huntington Beach, California, USA

“An R book for high schoolers! This is an excellent idea, and the quality of the product is equally excellent. It may be suitable for non-calculus-based introductory courses at the college level as well. … Dennis does a good job dispelling the ‘steep learning curve’ myth concerning R … . The writing style is clear and lively, and the examples should appeal to high school students. It is high time that introductory statistics be taught in an engaging manner that reflects our own enthusiasm for the subject, with meaningful data sets, attractive graphics, and so on. Dennis’ book is a fine contribution toward that goal.”

“Making the transition from student to professional researcher can be a daunting experience. This book can serve as a valuable refresher on hypothesis testing, coping with variation, data collection, sample size decisions, and more, along with cursory explanation of R output largely based on freely available data sets. … This is high-level material to aid the reader in becoming a confident researcher … . For the reader who wants to put theory to practice, and do it in R, this work can be a guide to success in analyzing and collection categorical data, detecting confounding, bootstrap approaches, case-control and cohort studies, and more.”

—Tom Schulte, MAA Reviews, April 2013

• Provides a step-by-step prescriptive guide to the data collection, data analysis, design, modeling, and reporting of results involved in clinical trials, experiments, and surveys • Describes contemporary statistical procedures, including bootstrap, decision trees, quantile regression, and permutation tests • Explains how to prepare graphs, tables, and oral presentations • Includes R code to implement the methods, along with a primer on R for those unfamiliar with the software

Selected Contents: Research Essentials. Planning: Hypotheses and Losses. Coping with Variation. Experimental Design.

Data Collection: Fundamentals. Quality Control.

Analyzing Your Data: Describing the Data.

Hypothesis Tests. Multiple Variables and Multiple Tests. Miscellaneous Hypothesis Tests. Sample Size Determination. Building a Model: Ordinary Least Squares. Alternate Regression Methods. Decision Trees. Reporting Your Results: Reports. Oral Presentations. Better Graphics. Nonrandom Samples: Cohort and Case-Control Studies. R Primer.

Bibliography. Indices.

Catalog no. K14287, August 2012, 269 pp.

Soft Cover, ISBN: 978-1-4398-9737-9 $49.95 / £31.99

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—Norman Matloff, Journal of Statistical Software, February 2013

• Illustrates how to calculate and graph examples in R using the main topics of precalculus algebra and portions of precalculus statistics • Presents a set of computational exercises in R calculations designed to be performed cooperatively in groups or alone • Includes the most used features of R on a reference card in the back of the book • Offers material for all types of STEM courses • Assumes only a moderate amount of high school algebra • Approaches R as a comprehensive tool for scientific computing, not just as a statistics package

Selected Contents: Introduction: Getting Started with R. R Scripts.

Functions. Basic Graphs. Data Input and Output.

Loops. Logic and Control. Quadratic Functions.

Trigonometric Functions. Exponential and Logarithmic Functions. Matrix Arithmetic. Systems of Linear Equations. Advanced Graphs. Probability and Simulation. Fitting Models to Data. Conclusion—It Doesn’t Take a Rocket Scientist. Appendices. Index.

Catalog no. K13498, September 2012, 360 pp.

Soft Cover, ISBN: 978-1-4398-7540-7 $39.95 / £25.99

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Statistical Theory & Methods New!

New!

Nonparametric Methods in Statistics with SAS Applications

Statistical Methods for Handling Incomplete Data

Olga Korosteleva

Jae Kwang Kim and Jun Shao

California State University, Long Beach, USA

Designed for a graduate course in applied statistics, this classroom-tested book teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods.

Drawing on data sets from the author’s many consulting projects, the text includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author’s website.

Along with many examples, this text covers the most upto-date statistical theories and computational methods for analyzing incomplete data. Suitable for graduate students, it presents a thorough treatment of statistical theories of likelihood-based inference with missing data. It also discusses numerous computational techniques and theories on imputation and extensively covers methods involving propensity score weighting, nonignorable missing data, longitudinal missing data, survey sampling, and statistical matching. Some of the research ideas introduced can be developed further for specific applications.

• Uses the mean score equation as a building block for developing the theory for missing data analysis

• Covers a variety of nonparametric techniques for hypotheses testing, smoothing, survival analysis, estimation, and more

• Provides comprehensive coverage of computational techniques for missing data analysis

• Contains end-of-chapter exercises and abundant worked examples from the health and social sciences

• Presents a rigorous treatment of imputation techniques, particularly fractional imputation

• Includes complete SAS codes for all examples • Provides data sets for exercises on the author’s website Solutions manual and figure slides available upon qualifying course adoption

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

Resampling Methods for Interval Estimation.

Appendices. Recommended Books. Index of Notation. Index.

Catalog no. K18845, September 2013, 195 pp.

Soft Cover, ISBN: 978-1-4665-8062-6 $69.95 / £44.99 Also available as an eBook

• Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data • Describes a survey sampling application

Selected Contents: Introduction. Likelihood-Based Approach.

Computation. Imputation. Propensity Scoring Approach: Regression Weighting Method.

Propensity Score Method. Optimal Estimation.

Doubly Robust Method. Empirical Likelihood Method. Nonparametric Method. Nonignorable Missing Data. Longitudinal and Clustered Data.

Application to Survey Sampling: Calibration Estimation. Propensity Score Weighting Method.

Fractional Imputation. Fractional Hot Deck Imputation. Imputation for Two-Phase Sampling.

Synthetic Imputation. Statistical Matching.

Bibliography. Index.

Catalog no. K12249, July 2013, 223 pp.

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

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

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Statistical Theory & Methods New!

Stationary Stochastic Processes for Scientists and Engineers Georg Lindgren, Holger Rootzen, and Maria Sandsten “This book is a lucid and well-paced introduction to stationary stochastic processes, superbly motivated and illustrated through a wealth of convincing applications in science and engineering. It offers a clear guide to the formulation and mathematical properties of these processes and to some non-stationary processes too, without going too deeply into the mathematical foundations; the emphasis throughout is on practical application rather than mathematical development for its own sake. The reader will find tools for analysis and calculation and also—importantly—material to deepen understanding and generate enthusiasm and confidence.

An outstanding text.”

—Clive Anderson, University of Sheffield

Based on a course taught to undergraduate students in engineering for over 30 years, this textbook presents all the material for a first course in stationary stochastic processes (SSP). Following naturally from a mathematical statistics course, it covers model building via SSP with a focus on engineering applications.

• Presents covariance and Fourier methods, including linear filters in discrete and continuous time • Describes statistical estimation techniques, mainly for frequency analysis • Incorporates many exercises and MATLAB® -based lab sessions Solutions manual and figure slides available upon qualifying course adoption

Selected Contents: Stochastic Processes. Stationary Processes. The Poisson Process and Its Relatives. Spectral Representations. Gaussian Processes. Linear Filters— General Theory. AR, MA, and ARMA Models. Linear Filters—Applications. Frequency Analysis and Spectral Estimation. Appendices. References. Index.

Catalog no. K20279, September 2013, 328 pp.

ISBN: 978-1-4665-8618-5, $79.95 / £49.99 Also available as an eBook

Stationary Stochastic Processes Theory and Applications Georg Lindgren Lund University, Sweden

“In many respects, Lindgren’s Stationary Stochastic Processes: Theory and Applications is an updated and expanded version that has captured much of the same spirit (and topics!) as the Cramer and Leadbetter classic. While there have been a number of new and good books published recently on spatial statistics, none cover some of the key important topics such as sample path properties and level crossings in a comprehensive and understandable fashion like Lindgren’s book. This book is required reading for all of my PhD students working in spatial statistics and related areas.”

—Richard A. Davis, Columbia University

“Without being mathematically over-demanding, the book builds up the relevant theory in a very intuitive yet rigorous way that helps the reader to a deeper understanding of definitions and results that could otherwise be mystifying.”

—Claudia Klüppelberg and Morten Grud Rasmussen, Technische Universität München

• Presents all the basic theory together with recent research developments • Takes a rigorous and application-oriented approach to stationary processes • Explains how the basic theory is used in applications, including detection theory and signal processing, spatial statistics, and reliability • Provides the foundation for instructors to expand on topics, such as extreme value theory, filter theory, long-range dependence, and point processes • Includes many exercises and examples to illustrate the theory

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

Catalog no. K15489, October 2012, 375 pp.

ISBN: 978-1-4665-5779-6, $89.95 / £57.99 Also available as an eBook

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Statistical Theory & Methods New! New!

Exercises and Solutions in Statistical Theory Lawrence L. Kupper, Brian H. Neelon, and Sean M. O’Brien This book helps students obtain an in-depth understanding of statistical theory by working on and reviewing solutions to interesting and challenging exercises of practical importance. It improves students’ comprehension of the principles of statistical theory and helps them see how the principles can be used in practice.

Unlike similar books, this one incorporates many exercises that apply to real-world settings and provides much more thorough solutions. The exercises and selected detailed solutions cover from basic probability theory through to the theory of statistical inference. By mastering the theoretical statistical strategies necessary to solve the exercises, students will be prepared to successfully study even higher-level statistical theory.

• Presents numerous exercises relevant to real-life contexts • Contains very detailed solutions to half of the exercises • Enables instructors to use the material as classroom examples, homework problems, or examination questions • Requires a working understanding of multivariable calculus and basic knowledge of matrices • Provides exercises of varying levels of difficulty, including challenging exercises identified with an asterisk • Offers a detailed summary in the first chapter of all the statistical concepts needed to solve the exercises in the remainder of the book • Includes a section on useful mathematical results Solutions manual available upon qualifying course adoption

Selected Contents: Concepts and Notation. Basic Probability Theory.

Univariate Distribution Theory. Multivariate Distribution Theory. Estimation Theory. Hypothesis Testing Theory.

Catalog no. K16626, June 2013, 388 pp.

Soft Cover, ISBN: 978-1-4665-7289-8 $59.95 / £38.99 Also available as an eBook

Understanding Advanced Statistical Methods Peter Westfall Texas Tech University, Lubbock, USA

Kevin S.S. Henning Sam Houston State University, Huntsville, Texas, USA

“This book is unique in the way it approaches this topic. It does not subscribe to the cookbook template of teaching statistics but focuses instead on understanding the distinction between the observed data and the mechanisms that generated it. This focus allows a better distinction between models, parameters, and estimates and should help pave a way to instill statistical thinking to undergraduate students.”

—Mithat Gönen, Memorial Sloan-Kettering Cancer Center Solutions manual available upon qualifying course adoption

Catalog no. K14873, April 2013, 569 pp.

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

New!

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

Ya'acov Ritov The Hebrew University of Jerusalem, Israel

Selected Contents: Introduction. Point Estimation. Confidence Intervals, Bounds, and Regions. Hypothesis Testing. Asymptotic Analysis: Convergence and consistency in MSE. Convergence and consistency in probability. Convergence in distribution. The central limit theorem. Asymptotically normal consistency.

Asymptotic confidence intervals. Asymptotic normality of the MLE. Multiparameter case. Asymptotic distribution of the GLRT. Wilks’ theorem. Bayesian Inference. Elements of Statistical Decision Theory: Risk function and admissibility. Minimax risk and minimax rules. Bayes risk and Bayes rules. Posterior expected loss and Bayes actions. Admissibility and minimaxity of Bayes rule. Linear Models.

Appendices. Index.

Catalog no. K12383, April 2013, 240 pp.

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

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

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Statistical Theory & Methods Practical Multivariate Analysis Fifth Edition Abdelmonem Afifi, Susanne May, and Virginia A. Clark “I found the text enjoyable and easy to read. The authors provide a sufficient description of all the methodology for practical use. Each chapter includes at least one real-world dataset analysis and the software commands summary tables included at the end of every chapter should be particularly helpful to a practitioner of statistics. … I would recommend the text for practitioners of statistics … particularly those performing basic analysis in the health sciences.”

—Thomas J. Fisher, Journal of Biopharmaceutical Statistics, 2012 Solutions manual available upon qualifying course adoption

Catalog no. K10864, July 2011, 537 pp.

ISBN: 978-1-4398-1680-6, $93.95 / £46.99 Also available as an eBook

Applied Categorical and Count Data Analysis Wan Tang, Hua He, and Xin M. Tu University of Rochester, New York, USA

This self-contained text includes exercises at the end of each chapter, many real data examples, and sample programming codes in SAS, SPSS, and STATA for model implementations, enabling students to immediately experiment with the data in the examples. The codes are available on a supporting website.

Selected Contents: Introduction. Contingency Tables. Sets of Contingency Tables. Regression Models for Categorical Response. Regression Models for Count Response. Loglinear Models for Contingency Tables.

Analyses of Discrete Survival Time. Longitudinal Data Analysis. Evaluation of Instruments. Analysis of Incomplete Data. References. Index.

Catalog no. K10311, June 2012, 384 pp.

ISBN: 978-1-4398-0624-1, $89.95 / £57.99 Also available as an eBook

Coming soon!

Generalized Linear Mixed Models Modern Concepts, Methods and Applications Walter W. Stroup University of Nebraska, Lincoln, USA

“Walter Stroup is a leading authority on GLMMs for applied statisticians, especially as implemented in the SAS programming environment. He offers a thorough, engaging, and opinionated treatment of the subject … I found the ‘fully general’ GLMM approach to modeling and design issues (Chapters 1 and 2) to be quite illuminating. … Prospective readers without current access to SAS will be pleased to know that a reasonable level of access to SAS is now available at no cost to students and teachers on the web …”

—Homer White, MAA Reviews, June 2013

Catalog no. K10775, September 2012, 555 pp.

ISBN: 978-1-4398-1512-0, $89.95 / £57.99

Linear Algebra and Matrix Analysis for Statistics Sudipto Banerjee University of Minnesota, Minneapolis, USA

Anindya Roy University of Maryland Baltimore County, USA

“This beautifully written text is unlike any other in statistical science. It starts at the level of a first undergraduate course in linear algebra and takes the student all the way up to the graduate level, including Hilbert spaces. … The book is compactly written and mathematically rigorous, yet the style is lively as well as engaging. This elegant, sophisticated work will serve upper level and graduate statistics education well.”

—Jim Zidek, University of British Columbia

Catalog no. K10023, February 2014, c. 416 pp.

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

Also available as an eBook

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Statistical Theory & Methods Introduction to Statistical Limit Theory Alan M. Polansky Northern Illinois University, Dekalb, USA

Selected Contents: Sequences of Real Numbers and Functions. Random Variables and Characteristic Functions. Convergence of Random Variables. Convergence of Distributions.

Convergence of Moments. Central Limit Theorems.

Asymptotic Expansions for Distributions. Asymptotic Expansions for Random Variables. Differentiable Statistical Functionals. Parametric Inference.

Nonparametric Inference. Appendices. References.

Solutions manual available upon qualifying course adoption

Catalog no. C6604, January 2011, 645 pp.

ISBN: 978-1-4200-7660-8, $93.95 / £59.99 Also available as an eBook

Principles of Uncertainty Joseph B. Kadane Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

2011 Degroot Prize Winner “In this remarkable book, Kadane begins at the most rudimentary level, develops all the needed mathematics on the fly, and still manages to flesh out at least the core of the whole story, slowly, thoughtfully, and rigorously, right up to graduate level. Major theorems all proved in detail appear here, but not for their own sake; the author always carefully selects them to clarify the basic meaning of the subject … Highly recommended.”

—D.V. Feldman, CHOICE, February 2012

Catalog no. K12848, May 2011, 503 pp.

ISBN: 978-1-4398-6161-5, $89.95 / £59.99 Also available as an eBook

Applied Time Series Analysis

New!

Wayne A. Woodward, Henry L. Gray, and Alan C. Elliott

Theories, Algorithms, and Examples

“… impressive coverage … an excellent textbook to adopt for a class and a good introductory book for a student who wants to embark on dissertation research in time series. … the book provides the reader with very good background material to be able to conduct practical and insightful data analysis and be able to comprehend the more theoryoriented books. There are many very good exercises in this book …”

—Hernando Ombao, Journal of the American Statistical Association, March 2013

Catalog no. K10965, October 2011, 564 pp.

ISBN: 978-1-4398-1837-4, $99.95 / £60.99 Also available as an eBook

Data Mining Nong Ye Arizona State University, Phoenix, USA

“… provides full-spectrum coverage of the most important topics in data mining.

By reading it, one can obtain a comprehensive view on data mining, including the basic concepts, important problems in the area, and how to handle these problems. The whole book is presented in a way that a reader who does not have much background knowledge of data mining can easily understand. You can find many figures and intuitive examples in the book.”

—Zheng Zhao, SAS Institute Inc.

Catalog no. K10414, August 2013, 349 pp.

ISBN: 978-1-4398-0838-2, $119.95 / £76.99 Also available as an eBook

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Computational Statistics New!

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

Praise for the First Edition: “… students of all majors will benefit from the author’s fine presentation of applied probability models and computer simulation. I am seriously considering adopting it for a [probability-oriented course] … the chapters on simulation and applied probability models are truly outstanding …”

—Matthew A. Carlton, The American Statistician, August 2008

“This book is primarily intended for junior undergraduate to beginning graduate-level students majoring in computer-related fields. It can also be used by electrical engineering, mathematics, statistics, actuarial science, and other majors for a standard introductory statistics course. Graduate students can use this book to prepare for probabilitybased courses … this well-written text can be used as a standard reference on probability and statistical methods, simulation, and modeling tools.”

—Journal of the Royal Statistical Society

This second edition offers a new axiomatic introduction of probability, expanded coverage of statistical inference, more exercises at the end of each chapter, and additional MATLAB® codes, particularly new commands of the Statistics Toolbox. The text helps students understand general methods of stochastic modeling, simulation, and data analysis; make optimal decisions under uncertainty; model and evaluate computer systems and networks; and prepare for advanced probability-based courses.

• Leads students from probability fundamentals to stochastic processes, Markov chains, queuing systems, and Monte Carlo methods—topics frequently missing from standard textbooks • Satisfies the ABET requirements for probability and statistics • Provides MATLAB codes for simulation, computation, and data analysis • Contains many detailed examples that have direct applications to computer science and related areas • Summarizes the main concepts at the end of each chapter and reviews the necessary calculus and linear algebra in the appendix • Presents over 260 exercises for homework assignments and self-training—including 60 new to this edition Solutions manual and figure slides available upon qualifying course adoption

Catalog no. K13525, August 2013, 473 pp.

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

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Selected Contents: Introduction and Overview Probability and Random Variables Probability Sample space, events, and probability Rules of Probability Equally likely outcomes. Combinatorics ...

Discrete Random Variables and Their Distributions Distribution of a random variable Distribution of a random vector Expectation and variance ...

Continuous Distributions Probability density Families of continuous distributions Central limit theorem Computer Simulations and Monte Carlo Methods Introduction Simulation of random variables Solving problems by Monte Carlo methods Stochastic Processes Stochastic Processes Definitions and classifications Markov processes and Markov chains Counting processes ...

Queuing Systems Main components of a queuing system The Little’s Law Bernoulli single-server queuing process ...

Statistics Introduction to Statistics Population and sample, parameters and statistics Simple descriptive statistics ...

Statistical Inference I Parameter estimation Confidence intervals Unknown standard deviation ...

Statistical Inference II Chi-square tests Nonparametric statistics Bootstrap ...

Regression Least squares estimation Analysis of variance, prediction, and further inference Multivariate regression ...

Appendix Inventory of distributions Distribution tables ...

Index

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Computational Statistics R for Statistics Pierre-Andre Cornillon, Arnaud Guyader, Francois Husson, Nicolas Jegou, Julie Josse, Maela Kloareg, Eric Matzner-Lober, and Laurent Rouvière “Section 4.2 on the apply family of functions and related functions for matrices, arrays, and data frames is by far the most friendly and helpful introduction to the subject that I have seen. … If you are not a trained programmer but you aspire to write code that is efficient and perhaps, from time to time, clever, then this book is a fine place for you to start learning R.” —Homer S. White, MAA Reviews, January 2013

“… accessible for statisticians of all levels and areas of expertise as well as for novice and advanced R users. … I recommend it for anyone who wants to learn about the why and how of the most commonly employed statistical methods and their extensions.”

—Irina Kukuyeva, Journal of Statistical Software, November 2012

• Offers a basic introduction to the use of R for statistical analysis • Includes detailed information on installing and getting started with R • Presents material on data visualization, graphics, and programming • Covers regression, ANOVA, principal component analysis, clustering, and other techniques • Uses an example-based approach to data analysis, enabling students to replicate the analyses using their own data • Provides code and data sets on CRAN

Selected Contents: An Overview of R: Main Concepts. Preparing Data.

R Graphics. Making Programs with R. Statistical Methods: Introduction to the Statistical Methods.

A Quick Start with R. Hypothesis Test. Regression.

Analysis of Variance and Covariance. Classification.

Exploratory Multivariate Analysis. Clustering.

Appendix.

Catalog no. K13834, March 2012, 320 pp.

Soft Cover, ISBN: 978-1-4398-8145-3 $59.95 / £34.99 Also available as an eBook

The BUGS Book A Practical Introduction to Bayesian Analysis David Lunn, Chris Jackson, Nicky Best, Andrew Thomas, and David Spiegelhalter “… the tens of thousands of users of WinBUGS are indebted to the leading team of the BUGS project for having eventually succeeded in finalizing the writing of this book and for making sure that the long-held expectations are not dashed. … strikes the right distance between advanced theory and pure practice. … The BUGS Book is not only a major textbook on a topical subject, but it is also a mandatory one for all statisticians willing to learn and analyze data with Bayesian statistics at any level. …”

—Jean-Louis Fouley, CHANCE, 2013

“… a two-in-one product that provides the reader with both a BUGS manual and a Bayesian analysis textbook, a combination that will likely appeal to many potential readers. … The strength of The BUGS Book is its rich collection of ambitiously constructed and thematically arranged examples, which often come with snippets of code and printouts, as well as illustrative plots and diagrams. … great value to many readers seeking to familiarize themselves with BUGS and its capabilities.”

—Joakim Ekström, Journal of Statistical Software, January 2013

• Covers all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity • Features a large number of worked examples and applications from a wide range of disciplines • Includes detailed exercises and solutions on the supporting website

Selected Contents: Introduction: Probability and Parameters. Monte Carlo Simulations using BUGS. Introduction to Bayesian Inference. Introduction to Markov Chain Monte Carlo Methods. Prior Distributions. Regression Models. Categorical Data. Model Checking and Comparison. Issues in Modeling. Hierarchical Models. Specialized Models. Different Implementations of BUGS. Appendices.

Bibliography. Index.

Catalog no. C8490, October 2012, 399 pp.

Soft Cover, ISBN: 978-1-58488-849-9 $49.95 / £24.99 Also available as an eBook

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

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Computational Statistics Statistical Computing in C++ and R

A Gentle Introduction to Stata

Randall L. Eubank

Revised Third Edition

Arizona State University, Tempe, USA

Ana Kupresanin Lawrence Livermore National Laboratory, California, USA

Parallel processing can be ideally suited for the solving of more complex problems in statistical computing. This book discusses code development in C++ and R, before going beyond to look at the valuable use of these two languages in unison. It covers linear equation solution with regression and linear models motivation, optimization with maximum likelihood and nonlinear least squares motivation, and random number generation. While the text does require a working knowledge of basic concepts in statistics and experience in programming, it does not require knowledge specific to C++ or R.

Alan C. Acock Oregon State University, Corvallis, USA

Updated to reflect the new features of Stata 11, this third edition continues to help new Stata users become proficient in Stata.

After reading this introductory text, students will be able to enter, build, and manage a data set as well as perform fundamental statistical analyses. This edition includes a new chapter on the analysis of missing data and the use of multiple-imputation methods. It also provides an extensive revision of the chapter on ANOVA, along with additional material on the application of power analysis. Each chapter includes exercises and real data sets are used throughout.

• Reflects the new features of Stata 11

• Integrates both C++ and R for the solution of statistical computing problems

• Shows how to enter, build, and manage a data set

• Covers object-oriented programming in both languages

• Supplements basic statistical modeling topics with discussions of effect sizes and standardized coefficients

• Uses C++ code in R and R functions in the C++ program • Presents applications of the C++ Standard Template Library for statistical computing purposes • Provides an introduction to parallel processing in C++ and R

Selected Contents: Introduction. Computer Representation of Numbers A Sketch of C++ Generation of Pseudo-Random Numbers Programming in R Creating Classes and Methods in R Numerical Linear Algebra Numerical Optimization Abstract Data Structures Data Structures in C++ Parallel Computing in C++ and R An Introduction to Unix An Introduction to R C++ Library Extensions (TR1) The Matrix and Vector Classes The ranGen Class References Index Catalog no. C6650, December 2011, 556 pp.

ISBN: 978-1-4200-6650-0, $89.95 / £59.99 Also available as an eBook

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• Discusses various model selection criteria, including semipartial correlations • Employs real data sets, such as the General Social Surveys from 2002 and 2006

Selected Contents: Support Materials Getting Started Entering Data Preparing Data for Analysis Working with Commands, Do-Files, and Results Descriptive Statistics and Graphs for One Variable Statistics and Graphs for Two Categorical Variables Tests for One or Two Means Bivariate Correlation and Regression Analysis of Variance Multiple Regression Logistic Regression Measurement, Reliability, and Validity Working with Missing Values—Multiple Imputation Appendix References Author Index Subject Index Catalog no. N10594, March 2012, 401 pp.

Soft Cover, ISBN: 978-1-59718-109-9 $79.95 / £49.99

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Biostatistics New!

Survival Analysis in Medicine and Genetics Jialiang Li National University of Singapore, Singapore

Shuangge Ma Yale University, New Haven, Connecticut, USA

Using real data sets throughout, this text introduces the latest methods for analyzing high-dimensional survival data. With an emphasis on the applications of survival analysis techniques in genetics, it presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. The book reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.

• Explains how to analyze censored survival time data in medical and genetic research • Provides a pedagogical introduction to time-dependent diagnostic accuracy studies • Covers recent high-dimensional data analysis and variable selection methods • Introduces nonparametric regression for survival analysis and the Fine-Gray model for competing risks data • Includes exercises in each chapter • Offers lecture slides on the book’s CRC Press web page

Selected Contents: Introduction: Examples and Basic Principles.

Analysis Trilogy: Estimation, Test, and Regression.

Analysis of Interval Censored Data. Special Modeling Methodology: Nonparametric Regression Multivariate Survival Data. Cure Rate Model.

Bayesian Analysis. Diagnostic Medicine for Survival Analysis. Survival Analysis with High-Dimensional Covariates: Applications. Identification of Marginal Association. Multivariate Prediction Models.

Incorporating Hierarchical Structures. Integrative Analysis. Bibliography. Index.

Medical Biostatistics Third Edition Abhaya Indrayan The third edition of this acclaimed book incorporates several new topics, including adaptive designs, path analysis, an alternative approach for assessing clinical agreement, Six Sigma in healthcare, and much more. It offers expanded coverage of survival analysis, ROC curves, equivalence assessment, ANOVA, and other topics. This edition also illustrates the use of SPSS in ANCOVA, repeated measures ANOVA, stepwise regression, quadratic regression, ROC curve (MedCalc), and survival analysis.

• Demonstrates how biostatistics can help manage many types of medical uncertainties • Presents step-by-step explanations of statistical methods, along with a large number of real-life examples and worked exercises • Provides guide charts to enable quick access to relevant statistical procedures • Illustrates how statistical methods can be used to handle various aspects of a medical research setup

Selected Contents: Medical Uncertainties. Basics of Medical Studies.

Sampling Methods. Designs of Observational Studies. Medical Experiments. Clinical Trials.

Numerical Methods for Representing Variation.

Presentation of Variation by Figures. Some Quantitative Aspects of Medicine. Clinimetrics and Evidence-based Medicine. Measurement of Community Health. Confidence Intervals, Principles of Tests of Significance, and Sample Size. Inference from Proportions. Relative Risk and Odds Ratio.

Inference from Means. Relationships: Quantitative Data. Relationships: Qualitative Dependent. Survival Analysis. Simultaneous Consideration of Several Variables. Quality Considerations. Statistical Fallacies.

Catalog no. K13952, August 2012, 1024 pp.

ISBN: 978-1-4398-8414-0, $129.95 / £82.00 Also available as an eBook

Catalog no. K14175, June 2013, 381 pp.

ISBN: 978-1-4398-9311-1, $99.95 / £63.99 Also available as an eBook

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

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Biostatistics Regression Models as a Tool in Medical Research

Multivariate Survival Analysis and Competing Risks

Werner Vach

Martin J. Crowder

Institute of Medical Biometry and Medical Informatics, Freiburg, Germany

Imperial College, University of London, UK

• Discusses how and when regression models can fail

Suitable for graduate students in statistics and biostatistics as well as those in the medical field, epidemiology, and social sciences, this book introduces univariate survival analysis and extends it to the multivariate case. It also covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods.

• Describes the basic principles behind statistical computations, with more mathematical details given in the appendices

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

• Offers the data sets, solutions to all exercises, and a short introduction to Stata on the author’s website

• Contains many real-world examples to illustrate methodology

• Helps students improve their understanding of the role of regression models in the medical field • Illustrates each technique with a concrete example, enabling students to better appreciate the properties and theory of the methods • Uses Stata to demonstrate the practical use of the models

Figure slides available upon qualifying course adoption

Selected Contents: THE BASICS: Why Use Regression Models? …

Common Pitfalls in Using Regression Models.

ADVANCED TOPICS AND TECHNIQUES: Some Useful Technicalities. Comparing Regression Coefficients. Power and Sample Size. The Selection of the Sample. The Selection of Covariates. Modeling Nonlinear Effects. Transformation of Covariates.

Effect Modification and Interactions. Applying Regression Models to Clustered Data. Applying Regression Models to Longitudinal Data. The Impact of Measurement Error. The Impact of Incomplete Covariate Data. RISK SCORES AND PREDICTORS: Risk Scores. Construction of Predictors. Evaluating the Predictive Performance. Outlook: Construction of Parsimonious Predictors. MISCELLANEOUS: Alternatives to Regression Modeling. Specific Regression Models. Specific Usages of Regression Models. What Is a Good Model? Final Remarks on the Role of Prespecified Models and Model Development. MATHEMATICAL DETAILS: Mathematics behind the Classical Linear Regression Model. Mathematics behind the Logistic Regression Model. The Modern Way of Inference. Mathematics for Risk Scores and Predictors. Bibliography. Index.

• Presents a clear style aimed at graduate students in statistics • Offers a supporting R package for the analyses, with some code in the book

Selected Contents: Univariate Survival Analysis: Survival Data. Survival Distributions. Frailty Models. Parametric Methods.

Discrete Time: Non- and Semi-Parametric Methods.

Continuous Time: Non- and Semi-Parametric Methods. Multivariate Survival Analysis: Multivariate Data and Distributions. Frailty and Copulas. Repeated Measure. Wear and Degradation.

Competing Risks: Continuous Failure Times and Their Causes. Parametric Likelihood Inference. Latent Failure Times: Probability Distributions. Discrete Failure Times in Competing Risks. Hazard-Based Methods for Continuous Failure Times. Latent Failure Times: Identifiability Crises. Counting Processes in Survival Analysis: Some Basic Concepts. Survival Analysis. Non- and Semi-Parametric Methods.

Catalog no. K13489, April 2012, 417 pp.

ISBN: 978-1-4398-7521-6, $99.95 / £63.99 Also available as an eBook

Catalog no. K15111, November 2012, 495 pp.

ISBN: 978-1-4665-1748-6, $89.95 / £57.99 Also available as an eBook

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Biostatistics Biostatistics A Computing Approach Stewart J. Anderson University of Pittsburgh, Pennsylvania, USA

With an emphasis on the importance of simulation, this book introduces modern and classical biostatistical methods and compares their respective usefulness. It covers essential topics in biostatistical science, including simple linear regression, multivariate regression, repeated measure, nonparametric analysis, survival analysis, sample size, and power calculations.

Assuming only basic knowledge of probability and statistics, the text offers numerous practical applications and detailed worked examples taken from the medical field, all computed using R and SAS. • Provides an introduction to important modern and classical methods used in biostatistics • Focuses on visualization and computational tools • Covers key topics in biostatistical science, including linear regression, multivariate regression, and repeated measures • Incorporates practical applications and worked examples from the medical area • Explains how to use SAS and R in the appendices • Includes end-of-chapter exercises with solutions

Selected Contents: Review of Topics in Probability and Statistics. Use of Simulation Techniques: What can we accomplish with simulations? How to employ a simple simulation strategy. Generation of pseudorandom numbers. Generating discrete and continuous random variables. … The Central Limit Theorem.

Correlation and Regression. Analysis of Variance.

Discrete Measures of Risk: Odds ratio and relative risk. Calculating risk in the presence of confounding.

Logistic regression. Using SAS and R for logistic regression. … Multivariate Analysis. Analysis of Repeated Measures Data. Nonparametric Methods. Analysis of Time to Event Data. Sample Size and Power Calculations. Appendices.

References. Index.

Catalog no. C8342, December 2011, 326 pp.

ISBN: 978-1-58488-834-5, $83.95 / £41.99 Also available as an eBook

Coming soon!

Epidemiology Study Design and Data Analysis, Third Edition Mark Woodward Mount Sinai Medical Center, New York, New York, USA

Praise for Previous Editions: “Mark Woodward’s excellent second edition will effectively serve post-graduate or advanced undergraduate students studying epidemiology, as well as statisticians or researchers who are regularly confronted with epidemiological questions.”

—Journal of the American Statistical Association

“… the most complete and practical introduction to the design and analysis of epidemiological studies I’ve encountered … an excellent text for either a course introducing epidemiologists to statistical thought and methods or a course introducing statisticians to epidemiological thought and methods … replete with understandable examples and worked exercises …”

—Dan McGee, Florida State University

Revised, expanded, and updated, this text covers the spectrum of statistical principles and analytical tools used in epidemiological research. This third edition includes two new chapters on clinical decision rules and computer-intensive methods. Along with more exercises and figures, it also contains new sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines.

• Requires little statistical background—ideal for students in epidemiology as well as biostatistics • Uses real case studies to illustrate principles more clearly • Covers systematic evaluation, linear mixed models, and case-cohort studies • Includes data sets for examples and exercises, SAS and Stata code for examples, a sample size calculator, and a SAS floating absolute risk macro on a supporting website

Selected Contents: Introduction. Basic Analytical Procedures. Assessing Risk Factors. Confounding and Interaction. Cohort Studies. Case-Control Studies. Intervention Studies.

Sample Size Determination. Modeling Quantitative Outcome Data. Modeling Binary Outcome Data.

Modeling Follow-Up Data.

Catalog no. K11828, October 2013, c. 816 pp.

ISBN: 978-1-4398-3970-6, $99.95 / £49.99 Also available as an eBook

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

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Statistical Genetics & Bioinformatics

Stochastic Modelling for Systems Biology Second Edition Darren J. Wilkinson

Statistics and Data Analysis for Microarrays Using R and Bioconductor Second Edition Sorin Dra˘ghici

Newcastle University, UK

Wayne State University, Detroit, Michigan, USA

Praise for the First Edition

Richly illustrated in color, this bestselling text provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that explains the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems. Now using R and Bioconductor, this edition has been expanded with 14 new chapters and 600 more pages.

“… well suited as an in-depth introduction into stochastic chemical simulation, both for self-study or as a course text …”

—Biomedical Engineering Online, December 2006

This book provides an accessible introduction to the use of stochastic processes for modeling biological systems, such as genetic and biochemical networks.

Focusing on simulation, the text includes many examples, R and SBML code, and a number of computerbased exercises.

Fully updated, this second edition includes improvements to the chapters on Markov processes, kinetics, and approximate algorithms. It also greatly expands the coverage of statistical inference using likelihoodfree techniques. Updated computing aspects include a new R package and the use of the new SBML Level 3.

• Provides the option of learning R in parallel with learning about data analysis • Covers background material for those with a limited mathematical, genetic, or molecular biology foundation • Includes R code on a CD-ROM

• Provides an accessible introduction to stochastic modeling for systems biology

Selected Contents:

• Focuses on computer simulation, with R and SBML code

Introduction. The Cell and Its Basic Mechanisms.

Microarrays. Reliability and Reproducibility Issues in DNA Microarray Measurements. Image Processing.

Introduction to R. Bioconductor: Principles and Illustrations. Elements of Statistics. Probability Distributions. Basic Statistics in R. Statistical Hypothesis Testing. Classical Approaches to Data Analysis. Analysis of Variance (ANOVA). Linear Models in R. Experiment Design. Multiple Comparisons. Analysis and Visualization Tools.

Cluster Analysis. Quality Control. Data Pre-Processing and Normalization. Methods for Selecting Differentially Regulated Genes. The Gene Ontology (GO). Functional Analysis and Biological Interpretation of Microarray Data. Uses, Misuses, and Abuses in GO Profiling. A Comparison of Several Tools for Ontological Analysis. Focused Microarrays— Comparison and Selection. ID Mapping Issues.

Pathway Analysis. Machine Learning Techniques.

The Road Ahead. References.

• Includes exercises and many biologically motivated examples • Presents enhanced material on statistical inference

Selected Contents: Modeling and Networks: Introduction to Biological Modeling. Representation of Biochemical Networks.

Stochastic Processes and Simulation: Probability Models. Stochastic Simulation. Markov Processes.

Stochastic Chemical Kinetics: Chemical and Biochemical Kinetics. Case Studies. Beyond the Gillespie Algorithm. Bayesian Inference: Bayesian Inference and MCMC. Inference for Stochastic Kinetic Models. Conclusions: SBML Models.

Catalog no. K11715, November 2011, 363 pp.

ISBN: 978-1-4398-3772-6, $93.95 / £59.99 Also available as an eBook

Catalog no. K10487, December 2011, 1036 pp.

ISBN: 978-1-4398-0975-4, $89.95 / £57.99 Also available as an eBook

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Statistics for Engineering & Physical Science Probability, Statistics, and Reliability for Engineers and Scientists Third Edition Bilal M. Ayyub and Richard H. McCuen University of Maryland, College Park, USA

The third edition of this bestselling text places increased emphasis on simulation, particularly as a modeling tool, applying it progressively with projects that continue in each chapter. This edition also features expanded discussions of ANOVA and a thorough treatment of Monte Carlo simulation. The authors not only clearly establish the limitations, advantages, and disadvantages of each method, but also show that data analysis is a continuum rather than the isolated application of different methods.

Solutions manual and PowerPoint slides available upon qualifying course adoption

Catalog no. K10476, April 2011, 663 pp.

ISBN: 978-1-4398-0951-8, $119.95 / £76.99 Also available as an eBook

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

“I think this will make an excellent introductory book on probability for engineers and it will prepare the IE, CE, and EE students for advanced courses that deal with random processes.”

— Edward A. Pohl, University of Arkansas

“The strength of the book is that it presents and translates the intuition concerning probability into mathematical structures using examples and explanations rather than the traditional approach of theorem and proof. … perfect for undergraduate engineering students looking for a textbook on probability.”

—Uday Kumar, Luleå University of Technology

“… this book takes a fresh approach to teaching undergraduate engineering students the fundamentals of probability. The book exploits students’ existing intuition regarding probabilistic concepts when presenting these concepts in a more rigorous manner.

Students should be better able to retain the knowledge gained through reading this text because of the relevance of the examples and applications.”

—Lisa Maillart, University of Pittsburgh

Applied Reliability Third Edition Paul A. Tobias Retired, Austin, Texas, USA

David Trindade Bloom Energy

This easy-to-use guide addresses basic descriptive statistics, reliability concepts, exponential distribution, Weibull distribution, and lognormal distribution. It also covers reliability data plotting, acceleration models, life test data analysis and systems models, and much more. The third edition includes a new chapter on Bayesian reliability analysis and expanded, updated coverage of repairable system modeling. Taking a practical and example-oriented approach to reliability analysis, the book provides detailed illustrations of software implementation throughout using several widely available software packages. Software and other files are available online.

Catalog no. C4665, August 2011, 600 pp.

ISBN: 978-1-58488-466-8, $93.95 / £63.99

• Presents the theory in an accessible manner, making the book suitable for a first course in probability theory • Includes numerous practical examples based on engineering applications • Uses set theory to introduce probability and its axioms • Covers conditional probability, independence, and approximations Solutions manual and PowerPoint slides available upon qualifying course adoption

Selected Contents: Historical Perspectives. A Brief Review of Set Theory.

Probability Basics. Random Variables and Distributions. Joint, Marginal, and Conditional Distributions. Expectation and Functions of Random Variables. Moment-Generating Functions.

Approximations and Limiting Behavior.

Appendix: Cumulative Poisson Probabilities. Index.

Catalog no. K14453, May 2012, 184 pp.

ISBN: 978-1-4665-0299-4, $129.95 / £82.00 Also available as an eBook

Also available as an eBook

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

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Statistics for Engineering & Physical Science Coming soon!

Introduction to Statistical Process Control Peihua Qiu University of Minnesota, Minneapolis, USA

Accessible to students in statistics and industrial engineering, this text presents a systematic introduction to traditional and modern SPC methods. Requiring some background in basic linear algebra, calculus, and introductory statistics, the book illustrates the methods using detailed worked examples from the author’s industrial experience. Pseudocode is provided for important methods and R code is available online. The text includes exercises at the end of each chapter, making it ideal as a course text or for self-study.

• Shows how SPC plays an important role in quality improvement • Discusses the development of many recent SPC methods for improving existing methods and handling new applications • Includes real industrial examples and end-ofchapter exercises • Incorporates pseudocode in the text and offers R code online

Selected Contents:

New!

Probabilistic Models for Dynamical Systems Second Edition Haym Benaroya, Seon Mi Han, and Mark Nagurka This book provides a selfcontained introduction to probabilistic modeling for dynamic systems, leading to advanced concepts. It introduces engineering students, especially those with an interest in dynamic systems, to randomness in variables, time-dependent functions, and solution methods of the governing equations. The text also offers extensive discussions on applications to vibrating systems, reliability, ocean engineering, and control/mechatronic systems. After completing this book, students will have a much better understanding of current research and be able to participate in advanced design.

• Offers an all-inclusive introduction to probability for engineering • Presents problems and discussions of the theory within each chapter • Introduces a variety of practical examples • Provides more than 300 illustrations • Adds extensive footnotes and biographies of historical figures Solutions manual and figure slides available upon qualifying course adoption

Introduction Basic Statistical Concepts and Methods Phase I Statistical Process Control

Selected Contents:

Shewhart Charts

Applications Events and Probability Random Variable Models Functions of Random Variables Random Processes Single Degree-of-Freedom Vibration Multi Degree-of-Freedom Vibration Continuous System Vibration Reliability Nonlinear and Stochastic Dynamic Models Non-Stationary Models Monte Carlo Methods Fluid-Induced Vibration Probabilistic Models in Controls and Mechatronic Systems Index.

CUSUM Charts Nonparametric Statistical Process Control: Multivariate Cases Profile Monitoring References Index Catalog no. K12137, October 2013, c. 524 pp.

ISBN: 978-1-4398-4799-2, $89.95 / £57.99 Also available as an eBook

Catalog no. K12264, May 2013, 764 pp.

ISBN: 978-1-4398-4989-7, $119.95 / £76.99 Also available as an eBook

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Statistics for Finance A Course on Statistics for Finance Stanley L. Sclove University of Illinois, Chicago, USA

This text presents statistical methods for financial investment analysis. Providing the connection between elementary statistics courses and quantitative finance courses, the book helps both existing and future quants improve their data analysis skills and better understand the modeling process. It incorporates both applied statistics and mathematical statistics and requires no prior background in finance. The author introduces regression analysis, time series analysis, and multivariate analysis step by step using models and methods from finance.

• Incorporates both applied statistics and mathematical statistics • Covers fundamental statistical concepts and tools, including averages, measures of variability, histograms, non-numerical variables, rates of return, and univariate, multivariate, two-way, and seasonal data sets

Monte Carlo Simulation with Applications to Finance Hui Wang Brown University, Providence, Rhode Island, USA

Requiring minimal background in mathematics and finance, this self-contained introduction includes numerous examples of option pricing, risk analysis, and sensitivity analysis. It also presents many hand-and-paper and MATLAB® coding exercises at the end of every chapter.

Selected Contents: Review of Probability. Brownian Motion. Arbitrage Free Pricing. Monte Carlo Simulation. Generating Random Variables. Variance Reduction Techniques.

Importance Sampling. Stochastic Calculus.

Simulation of Diffusions. Sensitivity Analysis.

Appendices. Bibliography. Index.

Catalog no. K12713, May 2012, 292 pp.

ISBN: 978-1-4398-5824-0, $79.95 / £49.99 Also available as an eBook

• Presents a careful development of regression, from simple to more complex models • Integrates regression and time series analysis with applications in finance • Requires no prior background in finance • Includes many exercises within and at the end of each chapter Figure slides available upon qualifying course adoption

Selected Contents: INTRODUCTORY CONCEPTS AND DEFINITIONS: Review of Basic Statistics. Stock Price Series and Rates of Return. Several Stocks and Their Rates of Return.

REGRESSION: Simple Linear Regression; CAPM and Beta. Multiple Regression and Market Models. PORTFOLIO ANALYSIS: Mean-Variance Portfolio Analysis.

Utility-Based Portfolio Analysis. TIME SERIES ANALYSIS: Introduction to Time Series Analysis. Regime Switching Models. Appendices. Index.

Catalog no. K14149, December 2012, 269 pp.

ISBN: 978-1-4398-9254-1, $89.95 / £57.99 Also available as an eBook

An Introduction to Exotic Option Pricing Peter Buchen University of Sydney, Australia

“The book presents an entertaining and captivating course in option pricing … . Thanks to the machinery developed by the author and his work group, pricing formulas for even the most complex exotic options are obtained from elementary pricing formulas using elegant arguments and simple algebraic manipulations, i.e., without lengthy integrations. … a very valuable treatise on exotic option pricing in a Black-Scholes economy. In addition, every chapter concludes with a set of highly relevant and inspiring exercises.”

—Tamás Mátrai, Zentralblatt MATH 1242 Solutions manual available upon qualifying course adoption

Catalog no. C9100, February 2012, 296 pp.

ISBN: 978-1-4200-9100-7, $79.95 / £49.99 Also available as an eBook

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Statistics for Finance Computational Methods in Finance Ali Hirsa Caspian Capital Management, LLC, New York, USA

“… there are several sections on topics that are rarely treated in textbooks: saddle point approximations, numerical solution of PIDEs, and others. There is also extensive material on model calibration, including interest rate models and filtering approaches.

The book is a very comprehensive and useful reference for anyone, even with limited mathematical background, who wishes to quickly understand techniques from computational finance.”

—Stefan Gerhold, Zentralblatt MATH 1260

Catalog no. K11454, September 2012, 444 pp.

ISBN: 978-1-4398-2957-8, $89.95 / £59.99 Also available as an eBook

Stochastic Finance A Numeraire Approach Jan Vecer Columbia University, New York, New York, USA

“Finally, we have a full volume with a systematic treatment of the change of numeraire techniques. Jan Vecer has taken years of teaching experience and practitioners’ feedback to unify a previously complicated topic into the most elegant and easily accessible numeraire textbook to come down the pike. Now it has become fun to learn about parity and duality relationships among exotic options in a whole variety of models. The practitioners will be happy about the dimension reduction methods. There should be more such books.”

—Uwe Wystup, Managing Director, MathFinance AG

Catalog no. K10632, January 2011, 342 pp.

ISBN: 978-1-4398-1250-1, $72.95 / £46.99 Also available as an eBook

Option Valuation A First Course in Financial Mathematics Hugo D. Junghenn The George Washington University, Washington, D.C., USA

Selected Contents: Interest and Present Value. Probability Spaces.

Random Variables. Options and Arbitrage.

Discrete-Time Portfolio Processes. Expectation of a Random Variable. The Binomial Model. Conditional Expectation and Discrete-Time Martingales.

The Binomial Model Revisited. Stochastic Calculus.

The Black-Scholes-Merton Model. Continuous-Time Martingales. The BSM Model Revisited. Other Options. Appendices. Bibliography. Index.

Solutions manual available upon qualifying course adoption

Catalog no. K14090, November 2011, 266 pp.

ISBN: 978-1-4398-8911-4, $59.95 / £38.99

Coming soon!

Quantitative Finance An Object-Oriented Approach in C++ Erik Schlogl University of Technology, Sydney, Australia

Selected Contents: A Brief Review of the C++ Programming Language.

Basic Building Blocks. Portfolio Optimization and Asset Pricing. Lattice Models. The Black-Scholes World. Finite Difference Methods for Partial Differential Equations. Implied Volatility and Implied Distributions. Monte Carlo Simulation. The HeathJarrow-Morton Model. The Lognormal Forward Rate “Market Models.” Case Studies of the ObjectOriented Approach.

Catalog no. C4797, December 2013, c. 350 pp.

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

Also available as an eBook

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Statistics for Biological Sciences

Coming soon!

Second Edition

Foundational and Applied Statistics for Biologists Using R

Malcolm Haddon

Ken A. Aho

CSIRO, Hobart, Tasmania, Australia

Idaho State University, Pocatello, Idaho, USA

“It is important to remember when reading this book that there are few texts that students can truly consult on fisheries science without a detailed understanding of stock assessment and fisheries management practices—this text continues to bridge that gap. The material has been revised and improvements made to a number of the examples. Two concerns and reservations that I commented on in my previous review have been addressed by the inclusion of two new chapters… . The book is lavishly illustrated throughout with the use of Microsoft Excel workbooks, which adds to the flexibility, availability, and ease of use of the text. I recommend the text both as a course companion and for private study.”

Taking a biological perspective, this text provides an introduction to foundational topics in science, probability, and mathematical statistics for advanced undergraduate and graduate students in biology and related fields. It offers comprehensive background material on probability and mathematical statistics, emphasizing Bayesian methods and biological applications throughout. The author discusses relevant issues in the philosophy of science as well as the design of effective experiments. He also uses an R package specifically developed to demonstrate ideas presented in the book.

Modelling and Quantitative Methods in Fisheries

—Carl M. O’Brien, International Statistical Review, 2012

• Introduces an array of ideas in modeling and quantitative methods that have direct relevance to fisheries science, biological modeling, ecology, and population dynamics

• Provides a foundation for biostatistics research • Describes ideas in probability and mathematical statistics in a gentle way • Presents many biological applications • Builds on mathematical/statistical ideas as the text progresses

• Incorporates two new chapters on characterizing uncertainty and size-based models

• Contains problem sets and supplemental exercises in each chapter

• Includes many worked examples in Excel that help explain the analyses in detail and demonstrate how to perform the analyses

• Uses the interactive graphical functions from the R package asbio

• Provides a set of example workbooks in Excel for download on the book’s CRC Press web page

Selected Contents:

Selected Contents: Introduction to Science Introduction to R Introduction to Probability

Fisheries and Modeling. Simple Population Models.

Model Parameter Estimation. Computer-Intensive Methods. Randomization Tests. Statistical Bootstrap Methods. Monte Carlo Modeling. Characterization of Uncertainty. Growth of Individuals. Stock Recruitment Relationships. Surplus Production Models. Age-Structured Models. Size-Based Models.

Appendix. Bibliography. Index.

Probability Density Functions

Catalog no. C561X, March 2011, 465 pp.

ISBN: 978-1-58488-561-0, $83.95 / £41.99

Also available as an eBook

Parameters and Estimators Sampling Distributions and Associated Topics Hypothesis Testing Designing and Implementing Effective Research Catalog no. K13403, December 2013, c. 544 pp.

ISBN: 978-1-4398-7338-0, $69.95 / £44.99

Also available as an eBook

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

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

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

—Robert W. Hayden, MAA Reviews, March 2012

• Provides material for a two-semester, introductory course for graduate students in the social sciences • Focuses on conceptual issues rather than complex computational details • Presents many examples using data from actual studies, which illustrate the potential problems associated with methods routinely taught and used • Offers over 900 R functions • Includes solutions to selected exercises in an appendix

Selected Contents: Introduction. Numerical and Graphical Summaries of Data. Probability and Related Concepts. Sampling Distributions and Confidence Intervals. Hypothesis Testing. Regression and Correlation. Bootstrap Methods. Comparing Two Independent Groups.

Comparing Two Dependent Groups. One-Way ANOVA. Two-Way and Three-Way Designs.

Comparing More Than Two Dependent Groups.

Multiple Comparisons. Some Multivariate Methods.

Robust Regression and Measures of Association. Basic Methods for Analyzing Categorical Data. Answers to Selected Exercises. Tables. Basic Matrix Algebra.

References. Index.

Coming soon!

Nonparametric Statistics for Social and Behavioral Sciences Marie Kraska-Miller Auburn University, Alabama, USA

Designed for upper undergraduate and graduate students, this textbook covers the concepts, principles, and procedures used in performing nonparametric statistical procedures. It presents many demonstrations, examples, and exercises. SPSS screen shots are used to demonstrate steps of the procedures and interpret the results. Each chapter includes a summary, exercises, and references.

Solutions manual and figure slides available upon qualifying course adoption

Selected Contents: Introduction to Social Science Research. Introduction to Nonparametric Statistics. Analysis of Data to Determine Association and Agreement: Pearson Chi-Square Test of Association and Independence.

Contingency Coefficient. Phi Coefficient and Cram r Coefficient V. Kendall’s Taub and Kendall’s Tauc.

Kappa Statistic. Spearman Rank-Order Correlation Coefficient. Analyses for Two Independent Samples: Fisher Exact Test for 2 x 2 Tables. The Median Test. Wilcoxon-Mann-Whitney U Test.

Kolmogorov-Smirnov Two-Sample Test. HodgesLehman Estimate for Confidence Interval. Moses Extreme Reaction Test. Analysis of Multiple Independent Samples: Kruskal-Wallis One-Way Analysis of Variance by Ranks Test. Median Test— Extended. Jonckheere-Terpstra Test with Ordered Alternatives. Analysis of Two Dependent Samples: McNemar Change Test. The Sign Test for Two Related Samples. Wilcoxon Signed Rank Test.

Hodges-Lehman Estimate for Confidence Interval.

Analysis of Multiple Related Samples: The Cochran Q Test. The Friedman Analysis of Variance by Ranks Test. Kendall’s Coefficient of Concordance (W).

Analysis of Single Samples.

Catalog no. K14678, December 2013, c. 272 pp.

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

Catalog no. K11557, August 2011, 862 pp.

ISBN: 978-1-4398-3456-5, $93.95 / £52.99 Also available as an eBook

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1-800-634-7064 • 1-859-727-5000 • +44 (0) 1235 400 524 • orders@crcpress.com

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NEW!

Use as an Introduction for Graduate Students or as a Supplement in Advanced Courses • Helps students pick the best statistical method to analyze their survival data experiments • Provides statistical methods for right-censored and left-truncated survival data • Describes numerous techniques for regression modeling of competing risks data • Examines techniques for model selection and validation as well as the robustness of the Cox regression model • Discusses the estimation of models with more complex censoring and sampling schemes than simple right censoring • Presents multistate models for a patient’s complete disease/recovery process and multivariate models that have some dependency between a set of event times • Covers topics useful in the design and analysis of clinical trials where the outcome is the time to some event

View the full Table of Contents at www.crcpress.com Catalog no. K15384, August 2013, 656 pp., ISBN: 978-1-4665-5566-2, $99.95 / £63.99

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