Contents Statistics for Biology ..............................................3 Statistical Theory & Methods ................................5 Computational Statistics ......................................13 Page 3
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Statistics for Business & Finance ..........................14 Statistics for Engineering ......................................16 Biostatistics ..........................................................17 Statistics in Psychology & Social Sciences............18
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Environmental Statistics ......................................20 Statistics in Genetics & Biology............................21 Statistical Learning & Data Mining ......................22
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MBTST11 TMC 1.2111gtr
Statistics for Biology
New!
Modelling and Quantitative Methods in Fisheries Second Edition Malcolm Haddon CSIRO, Hobart, Tasmania, Australia
Praise for the First Edition
Contents
“The book is a good introduction to modeling for students and practitioners. … One helpful feature is the use of spreadsheet examples to illustrate the methods.” —Fisheries, 2002
With numerous real-world examples, this text provides students with an introduction to the analytical methods used by fisheries’ scientists and ecologists. By following the examples using Excel, students see the nuts and bolts of how the methods work and better understand the underlying principles. Excel workbooks will be available for download from CRC Press Online. In this second edition, the author has revised all chapters and improved a number of the examples. This edition also includes two entirely new chapters: • Characterization of Uncertainty covers asymptotic errors and likelihood profiles and develops a generalized Gibbs sampler to run a Markov chain Monte Carlo analysis that can be used to generate Bayesian posteriors • Sized-Based Models implements a fully functional size-based stock assessment model using abalone as an example This textbook continues to cover a broad range of topics related to quantitative methods and modelling. It offers a solid foundation in the skills required for the quantitative study of marine populations. Explaining important and relatively complex ideas and methods in a clear manner, the author presents full, step-by-step derivations of equations as much as possible to enable a thorough understanding of the models and methods.
Catalog no. C561X, February 2011, c. 471 pp. ISBN: 978-1-58488-561-0, $79.95
Fisheries and Modelling Fish Population Dynamics The Objectives of Stock Assessment Characteristics of Mathematical Models Types of Model Structure Simple Population Models Assumptions—Explicit and Implicit Density-Independent Growth Density-Dependent Models Responses to Fishing Pressure The Logistic Model in Fisheries Age-Structured Models Simple Yield-per-Recruit Model Parameter Estimation Models and Data Least Squared Residuals Nonlinear Estimation Likelihood Bayes’ Theorem Computer-Intensive Methods Resampling Randomization Tests Jackknife Methods Bootstrapping Methods Monte Carlo Methods Bayesian Methods Relationships between Methods Computer Programming Randomization Tests Statistical Bootstrap Methods Monte Carlo Modelling Characterization of Uncertainty Growth of Individuals Stock Recruitment Relationships Surplus Production Models Age-Structured Models Size-Based Models For more complete contents, visit www.crctextbooks.com
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3
Statistics for Biology
New!
Introduction to Statistical Data Analysis for the Life Sciences Claus Thorn Ekstrom and Helle Sorensen University of Copenhagen, Denmark
Developed from the authors’ courses at the University of Copenhagen, this textbook covers all the usual material but goes further than other texts. The authors imbue students with the ability to model and analyze data early in the text and then gradually fill in the blanks with needed probability and statistics theory. While the main text can be used with any statistical software, the authors encourage a reliance on R. They provide a short tutorial for students new to the software and include R commands and output at the end of each chapter. Ultimately, students come away with a computational toolbox that enables them to perform actual analysis for real data sets as well as the confidence and skills to undertake more sophisticated analyses as their careers progress.
Features • Includes numerous exercises, half of which can be done by hand • Contains ten case exercises that encourage students to apply their knowledge to larger data sets and learn more about approaches specific to the life sciences • Offers a tutorial for students new to R • Provides data sets used in the text on a supporting website • Emphasizes both data analysis and the mathematics underlying classical statistical analysis • Covers modeling aspects of statistical analysis with added focus on biological interpretations • Explores applications of statistical software in analyzing real-world problems and data sets Solutions manual available for qualifying instructors
Catalog no. K11221, January 2011, 427 pp. Soft Cover, ISBN: 978-1-4398-2555-6, $69.95
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Contents Description of Samples and Populations Data types Visualizing categorical data Visualizing quantitative data Statistical summaries What is a probability? Linear Regression Comparison of Groups Graphical and simple numerical comparison Between-group variation and within-group variation Populations, samples, and expected values Least squares estimation and residuals Paired and unpaired samples Perspective The Normal Distribution Properties One sample Are the data (approximately) normally distributed? The central limit theorem Statistical Models, Estimation, and Confidence Intervals Statistical models Estimation Confidence intervals Unpaired samples with different standard deviations Hypothesis Tests Model Validation and Prediction Linear Normal Models Multiple linear regression Additive two-way analysis of variance Linear models Interactions between variables Probabilities The Binomial Distribution The independent trials model The binomial distribution Estimation, confidence intervals, and hypothesis tests Differences between proportions Analysis of Count Data Logistic Regression Case Exercises For more complete contents, visit www.crctextbooks.com
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Statistical Theory & Methods
Statistical Theory & Methods An Introduction to Statistical Inference and Its Applications with R Michael W. Trosset Indiana University, Bloomington, USA
This self-contained introduction helps students understand basic procedures of statistical inference and how to use them. Emphasizing concepts rather than recipes, the text provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples—not to perform entire analyses. The heart of the text is a careful exposition of point estimation, hypothesis testing, and confidence intervals. The author also discusses the role of simulation in modern statistical inference.
Features • Explains how statistical methods are used for data analysis • Uses the elementary functions of R to perform the individual steps of statistical procedures • Includes amusing anecdotes and trivia, such as Ambrose Bierce’s definition of insurance • Introduces basic concepts of inference through a careful study of several important procedures, including parametric and nonparametric methods, analysis of variance, and regression • Presents many applications along with supporting data sets • Contains exercises at the end of each chapter • Offers the R code and data sets available for download online Solutions manual available for qualifying instructors
Catalog no. C9470, 2009, 496 pp. ISBN: 978-1-58488-947-2, $79.95
Contents Experiments Mathematical Preliminaries Probability Discrete Random Variables Continuous Random Variables Quantifying Population Attributes Data The Plug-In Principle Plug-In Estimates of Mean and Variance Plug-In Estimates of Quantiles Kernel Density Estimates Case Study: Are Forearm Lengths Normally Distributed? Transformations Lots of Data Averaging Decreases Variation The Weak Law of Large Numbers The Central Limit Theorem Inference A Motivating Example Point Estimation Heuristics of Hypothesis Testing Testing Hypotheses about a Population Mean Set Estimation 1-Sample Location Problems Case Study: Deficit Unawareness in Alzheimer’s Disease 2-Sample Location Problems Case Study: Etruscan versus Italian Head Breadth The Analysis of Variance Case Study: Treatments of Anorexia Goodness-of-Fit Association Bivariate Distributions Normal Random Variables Monotonic Association Explaining Association Case Study: Anorexia Treatments Revisited Simple Linear Regression Case Study: Are Thick Books More Valuable? Simulation-Based Inference Termite Foraging Revisited The Bootstrap Case Study: Adventure Racing For more complete contents, visit www.crctextbooks.com
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Statistical Theory & Methods New!
Bayesian Ideas and Data Analysis An Introduction for Scientists and Statisticians Ronald Christensen University of New Mexico, Albuquerque, USA
Wesley O. Johnson University of California, Irvine, USA
Adam J. Branscum Oregon State University, Corvallis, USA
Timothy E. Hanson University of South Carolina, Columbia, USA
Emphasizing the use of WinBUGS and R to analyze real data, this text presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. Offering flexible options for teaching a variety of courses, the book focuses on the necessary tools and concepts for modeling and analyzing scientific data. The first five chapters contain core material that spans basic Bayesian ideas, calculations, and inference. The text also covers Monte Carlo methods, regression, survival analysis, binary diagnostic testing, and nonparametric inference.
Features • Covers a large number of statistical models • Emphasizes the elicitation of reasonable prior information • Explores numerical approximations via simulation • Uses WinBUGS and R for computational problems • Reviews basic concepts of matrix algebra and probability • Includes numerous exercises and real-world examples throughout • Provides data, programming code, and other materials on a supplemental website
Catalog no. K10199, January 2011, 516 pp. ISBN: 978-1-4398-0354-7, $69.95
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Contents Fundamental Ideas I Integration versus Simulation WinBUGS I: Getting Started Method of Composition Monte Carlo Integration Posterior Computations in R Fundamental Ideas II Statistical Testing Exchangeability Likelihood Functions Sufficient Statistics Analysis Using Predictive Distributions Flat Priors Jeffreys’ Priors Bayes Factors Other Model Selection Criteria Normal Approximations to Posteriors Bayesian Consistency and Inconsistency Hierarchical Models Some Final Comments on Likelihoods Identifiability and Noninformative Data Comparing Populations Illustrations: Foundry Data Simulations Generating Random Samples Traditional Monte Carlo Methods Basics of Markov Chain Theory Markov Chain Monte Carlo Basic Concepts of Regression Illustration: FEV Data Binomial Regression Illustrations: Space Shuttle Data Linear Regression Illustrations: FEV Data Correlated Data Illustrations: Interleukin Data Count Data Illustrations: Ache Hunting Data Time to Event Data Illustrations: Leukemia Cancer Data Time to Event Regression Illustrations: Leukemia Cancer Data Binary Diagnostic Tests Illustrations: Coronary Artery Disease Nonparametric Models Illustrations: Galaxy Data For more complete contents, visit www.crctextbooks.com
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Statistical Theory & Methods New!
Nonparametric Statistical Inference
Logistic Regression Models
Fifth Edition
Joseph M. Hilbe
Jean Dickinson Gibbons and Subhabrata Chakraborti
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, and Arizona State University, Tempe, USA
University of Alabama, Tuscaloosa, USA
Praise for the Fourth Edition “… a valuable addition to every statistician’s library.” —ISI Short Book Reviews
“I learned nonparametric statistics … from the first author’s original version of the book. … a classic text that should be part of every statistician’s library. …” —Technometrics, May 2004
“… a good textbook for a beginning graduatelevel course in nonparametric statistics.” —Journal of the American Statistical Association
Read more reviews at CRC Press Online With at least 50 percent of the material revised, this edition covers the most commonly used nonparametric procedures. The authors carefully state the assumptions, develop the theory behind the procedures, and illustrate the techniques using examples from the social, behavioral, and life sciences. The text also contains many tables needed for finding P values and obtaining confidence interval estimates of parameters.
Contents Order Statistics, Quantiles, and Coverages. Tests of Randomness. Tests of Goodness of Fit. One-Sample and Paired-Sample Procedures. The General Two-Sample Problem. Linear Rank Statistics and the General Two-Sample Problem. Linear Rank Tests for the Location Problem. Linear Rank Tests for the Scale Problem. Tests of the Equality of k Independent Samples. Measures of Association for Bivariate Samples. Measures of Association in Multiple Classifications. Asymptotic Relative Efficiency. Analysis of Count Data. Summary. Appendix of Tables. Answers to Problems. References. Index. Catalog no. C7619, January 2011, 650 pp. ISBN: 978-1-4200-7761-2, $99.95
This text shows students how to use logistic regression and extended logistic models for research. It presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models.
Features • Examines the theoretical foundation of many logistic models, including binary, ordered, multinomial, panel, and exact • Describes how each type of model is established, interpreted, and evaluated as to its goodness of fit • Analyzes the models using Stata • Offers R code at the end of most chapters so that students can duplicate the output displayed in the text • Includes numerous exercises and real-world examples from the medical, ecological, physical, and social sciences • Provides the example data sets in Stata, R, Excel, SAS, SPSS, and Limdep formats at CRC Press Online Solutions manual available for qualifying instructors
Contents Concepts Related to the Logistic Model. Estimation Methods. Derivation of the Binary Logistic Algorithm. Model Development. Interactions. Analysis of Model Fit. Binomial Logistic Regression. Overdispersion. Ordered Logistic Regression. Multinomial Logistic Regression. Alternative Categorical Response Models. Panel Models. Other Types of LogisticBased Models. Exact Logistic Regression. Conclusion. Appendices. References. Indices. Catalog no. C7575, 2009, 656 pp. ISBN: 978-1-4200-7575-5, $79.95
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Statistical Theory & Methods New!
Design and Analysis of Experiments with SAS
Design of Experiments
John Lawson
Max Morris
Brigham Young University, Provo, Utah, USA
Covering both classical ideas in experimental design and the latest research topics, this text provides practical guidance on the computer analysis of experimental data. It connects the objectives of research to the type of experimental design required, describes the actual process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.
Features • Uses SAS 9.2 throughout to illustrate the construction of experimental designs and analysis of data • Shows how to display experimental results graphically using SAS 9.2 ODS graphics • Provides uniform coverage on experimental designs and design concepts that are most commonly used in practice • Presents many applications from the pharmaceutical, agricultural, industrial chemicals, and machinery industries • Includes exercises at the end of every chapter • Offers all the SAS code for examples on the author’s website Solutions manual available for qualifying instructors
Contents Completely Randomized Designs with One Factor. Factorial Designs. Randomized Block Designs. Designs to Study Variances. Fractional Factorial Designs. Incomplete and Confounded Block Designs. Split-Plot Designs. Crossover and Repeated Measures Designs. Response Surface Designs. Mixture Experiments. Robust Parameter Design Experiments. Experimental Strategies for Increasing Knowledge. Bibliography. Index. Catalog no. C6060, 2010, 596 pp. ISBN: 978-1-4200-6060-7, $99.95
An Introduction Based on Linear Models Iowa State University, Ames, USA
This text enables students to fully appreciate the fundamental concepts and techniques of experimental design as well as the real-world value of design. It gives them a profound understanding of how design selection affects the information obtained in an experiment.
Features • Discusses the explicit relationship between experimental design and the quality of data analysis • Presents the fundamental concepts and techniques of experimental design • Describes specific forms or classes of experimental designs • Contains an introduction to design for regression models • Performs calculations using R, with commands provided in an appendix • Incorporates actual experiments drawn from the scientific and technical literature • Includes many end-of-chapter exercises Solutions manual available for qualifying instructors
Contents Linear Statistical Models. Completely Randomized Designs. Randomized Complete Blocks and Related Designs. Latin Squares and Related Designs. Some Data Analysis for CRDs and Orthogonally Blocked Designs. Balanced Incomplete Block Designs. Random Block Effects. Factorial Treatment Structure. Split-Plot Designs. Two-Level Factorial Experiments: Basics. Two-Level Factorial Experiments: Blocking. Two-Level Factorial Experiments: Fractional Factorials. Factorial Group Screening Experiments. Regression Experiments: FirstOrder Polynomial Models. Regression Experiments: Second-Order Polynomial Models. Introduction to Optimal Design. Appendices. References. Index. Catalog no. C9233, January 2011, 370 pp. ISBN: 978-1-58488-923-6, $89.95
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Statistical Theory & Methods Time Series Modeling, Computation, and Inference
Applied Statistical Inference with MINITAB®
Raquel Prado University of California, Santa Cruz, USA
Sally A. Lesik
Mike West
Central Connecticut State University, New Britain, USA
Duke University, Durham, North Carolina, USA
“This book/CD-ROM package is intended for a first course on applied inference for undergraduates and graduates in any field that uses statistics. The text is written to be beginner-friendly and oriented toward practical use of statistics, with less emphasis on theory.”
This text integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduatelevel account of Bayesian time series modeling and analysis, state-of-the-art approaches to univariate and multivariate time series analysis, and emerging topics at research frontiers.
—Book News, June 2010
Through clear, step-by-step mathematical calculations, this text enables students to gain a solid understanding of how to apply statistical techniques using a statistical software program. Ideal for students in the social sciences, it shows how to implement basic inferential techniques in practice using MINITAB®. The book establishes the foundation for students to build on work in more advanced inferential statistics.
Features • Provides a complete integration of MINITAB throughout the text
Features • Covers the major areas of modern time series models and theory, including time and spectral domain and univariate and multivariate time series methods • Presents analyses of real time series data in numerous examples and case studies to illustrate the flexibility and practical impact of the models and methods
• Includes fully worked out examples so students can easily follow the calculations
• Discusses recent techniques for modeling time series data, such as dynamic graphical models, SMC methods, and nonlinear/ non-Gaussian dynamic models
• Offers data sets and a trial version of MINITAB on accompanying CD-ROMs
• Includes a collection of end-of-chapter exercises
• Contains a set of homework problems at the end of each chapter
• Offers many of the data sets, R and MATLAB® code, and other material on the authors’ websites
Solutions manual available for qualifying instructors
Contents Graphing Variables. Descriptive Representations of Data and Random Variables. Basic Statistical Inference. Simple Linear Regression. More on Simple Linear Regression. Multiple Regression Analysis. More on Multiple Regression. Analysis of Variance. Other Topics. Index. Catalog no. C6583, 2010, 464 pp. ISBN: 978-1-4200-6583-1, $89.95
Contents Notation, Definitions, and Basic Inference. Traditional Time Domain Models. The Frequency Domain. Dynamic Linear Models. State-Space Time-Varying Autoregressive Models. Sequential Monte Carlo Methods for State-Space Models. Mixture Models in Time Series. Topics and Examples in Multiple Time Series. Vector AR and ARMA Models. Multivariate DLMs and Covariance Models. Indices. Bibliography. Catalog no. C9336, 2010, 368 pp. ISBN: 978-1-4200-9336-0, $89.95
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Statistical Theory & Methods
New!
Introduction to General and Generalized Linear Models Henrik Madsen and Poul Thyregod Technical University of Denmark, Lyngby
Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. Bridging the gap between theory and practice for modern statistical model building, the book covers both general and generalized linear models using the same likelihood-based methods. It presents the corresponding/parallel results for the general linear models first, since they are easier to understand and often more well known. Each chapter contains examples and guidelines for solving the problems via R, although other software packages are also discussed.
Features • Enables a clear comparison between general and generalized linear models • Provides an accessible description of advanced concepts of generalized linear models • Covers Gaussian-based hierarchical models and hierarchical generalized linear models • Introduces new concepts for mixed effects models that allow greater flexibility in model building and the data structures • Illustrates the power of the methods through many real-world examples, including drug development, pollutant emissions, and transportation safety • Uses R throughout to solve the examples • Offers solutions, additional exercises, data sets, and lecture slides on the book’s website
Catalog no. C9155, January 2011, 316 pp. ISBN: 978-1-4200-9155-7, $79.95
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Contents The Likelihood Principle General Linear Models The multivariate normal distribution General linear models Estimation of parameters Likelihood ratio tests Tests for model reduction Collinearity Inference on parameters in parameterized models Model diagnostics: residuals and influence Analysis of residuals Representation of linear models Generalized Linear Models Types of response variables Exponential families of distributions Generalized linear models Maximum likelihood estimation Likelihood ratio tests Test for model reduction Inference on individual parameters Examples Mixed Effects Models Gaussian mixed effects model One-way random effects model More examples of hierarchical variation General linear mixed effects models Bayesian interpretations Posterior distributions Random effects for multivariate measurements Hierarchical models in metrology General mixed effects models Laplace approximation Hierarchical Models Introduction, approaches to modelling of overdispersion Hierarchical Poisson gamma model Conjugate prior distributions Examples of one-way random effects models Hierarchical generalized linear models Real-Life Inspired Problems Dioxin emission Depreciation of used cars Young fish in the North Sea Traffic accidents Mortality of snails For more complete contents, visit www.crctextbooks.com
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Statistical Theory & Methods Random Phenomena
New!
Introduction to Statistical Limit Theory Alan M. Polansky Northern Illinois University, Dekalb, USA
Helping students develop a good understanding of asymptotic theory, this classroom-tested book provides a thorough yet accessible treatment of common modes of convergence and their related tools used in statistics. It covers the necessary introductory material as well as modern statistical applications, exploring how the underlying mathematical and statistical theories work together.
Features • Presents a review of the relevant mathematical limit theory that is used throughout the book • Provides coverage of expansion theory, a topic not typically covered in asymptotic texts • Incorporates detailed proofs and explanations of the results • Uses examples to illustrate the application of asymptotic theory to modern statistical problems • Offers references for further reading as well as tips on using R as a tool for visualizing asymptotic results • Includes many end-of-chapter exercises and experiments, ranging in level of difficulty from easy to advanced Forthcoming solutions manual available for qualifying instructors
Contents Sequences of Real Numbers and Functions. Random Variables and Characteristic Functions. Convergence of Random Variables. Convergence of Distributions. Convergence of Moments. Central Limit Theorems. Asymptotic Expansions for Distributions. Asymptotic Expansions for Random Variables. Differentiable Statistical Functionals. Parametric Inference. Nonparametric Inference. Appendices. References. Catalog no. C6604, January 2011, 645 pp. ISBN: 978-1-4200-7660-8, $89.95
Fundamentals of Probability and Statistics for Engineers Babatunde A. Ogunnaike University of Delaware, Newark, USA
“…an excellent choice for anyone, educator or practitioner, wishing to impart or gain a fundamental understanding of probability and statistics from an engineering perspective.” —Dennis C. Williams, The American Institute of Chemical Engineers Journal
Features • Includes case studies on Mendel’s study of genetics, conditional probabilities in World War II Naval tactical decision making, and in vitro fertilization • Provides examples drawn from molecular biology, finance and business, and population demographics • Supplies review questions, exercises, application problems, and project assignments • Presents data sets online and on CD-ROM, with a 30-day MINITAB trial featuring reduced purchase/rental rate offer Solutions manual available for qualifying instructors
Contents FOUNDATIONS: Two Motivating Examples. Random Phenomena, Variability, and Uncertainty. PROBABILITY: Fundamentals of Probability Theory. Random Variables and Distributions. Multidimensional Random Variables. Random Variable Transformations. Application Case Studies I: Probability. DISTRIBUTIONS: Ideal Models of Discrete Random Variables. Ideal Models of Continuous Random Variables. Information, Entropy, and Probability Models. Application Case Studies II: In Vitro Fertilization. STATISTICS: Introduction to Statistics. Sampling. Estimation. Hypothesis Testing. Regression Analysis. Probability Model Validation. Nonparametric Methods. Design of Experiments. Application Case Studies III: Statistics. APPLICATIONS: Reliability and Life Testing. Quality Assurance and Control. Introduction to Multivariate Analysis. Appendix. Index. Catalog no. 44974, 2010, 1056 pp. ISBN: 978-1-4200-4497-3, $133.95
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Statistical Theory & Methods
Stochastic Processes An Introduction, Second Edition Peter W. Jones and Peter Smith Keele University, Staffordshire, UK
Based on the authors’ highly popular, well-established course, this concise, updated textbook discusses the modeling and analysis of random experiments using the theory of probability. The authors make the material accessible to students by avoiding specialized applications and instead highlighting simple applications and examples. The associated website contains Mathematica® and R programs that offer flexibility in creating graphs and performing computations.
Features • Illustrates discrete random processes through the classical gambler’s ruin problem and its variants • Covers continuous random processes, such as Poisson and general population models • Describes applications of probability to modeling problems in engineering, medicine, and biology • Uses Mathematica and R to solve both theoretical and numerical examples and produce many graphs • Includes over 50 worked examples and more than 200 end-of-chapter problems with selected answers at the back of the book • Provides Mathematica and R programs on the book’s website Solutions manual available for qualifying instructors
Contents Some Background on Probability. Some Gambling Problems. Random Walks. Markov Chains. Poisson Processes. Birth and Death Processes. Queues. Reliability and Renewal. Branching and Other Random Processes. Computer Simulations and Projects. Answers and Comments on End-of-Chapter Problems. Appendix. References and Further Reading. Index.
Modeling and Analysis of Stochastic Systems Second Edition Vidyadhar G. Kulkarni University of North Carolina, Chapel Hill, USA
“… an accessible, well paced, and very nicely presented book. The publishers are also to be commended on its nice production: it is the sort of book which is a pleasure to read. In all, it is an excellent textbook for use in introductory courses on stochastic processes.” —International Statistical Review (2010), 78, 3
Read more reviews at CRC Press Online This book covers the most important classes of stochastic processes used in the modeling of diverse systems. After mastering the material in the text, students will be well-equipped to build and analyze useful stochastic models for various situations. Along with new appendices that collect results from analysis and differential and difference equations, this edition contains a new chapter on diffusion processes with applications to finance. It also offers a more streamlined, application-oriented approach to renewal, regenerative, and Markov regenerative processes. MATLAB®-based programs can be downloaded from the author’s website and a solutions manual is available for qualifying instructors.
Contents Discrete-Time Markov Chains: Transient Behavior. DTMCs: First Passage Times. DTMCs: Limiting Behavior. Poisson Processes. Continuous-Time Markov Chains. Queueing Models. Renewal Processes. Markov Regenerative Processes. Diffusion Processes. Epilogue. Appendices. Answers to Selected Problems. References. Index. Catalog no. K10430, 2010, 563 pp. ISBN: 978-1-4398-0875-7, $99.95
Catalog no. K10004, 2010, 232 pp., Soft Cover ISBN: 978-1-4200-9960-7, $82.95
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Computational Statistics Computational Statistics Graphics for Statistics and Data Analysis with R
Computational Statistics
Kevin J. Keen
An Introduction to R
University of Northern British Columbia, Prince George, Canada
Günther Sawitzki
“The book is methodical and complete … Reading this book will give you the ability to recognize and create the majority of the named graphics of statistics …”
“… a fresh perspective on teaching statistics. … The book introduces its topics and the corresponding methodologies well. … the book is well put together and quite enjoyable for its purpose of serving a small course on computational statistics. …”
—Journal of Statistical Software, September 2010, Volume 36
Showing students how to use graphics to display or summarize data, this text provides best practice guidelines for producing and choosing among graphical displays. It also covers the most effective graphing functions in R. The author presents the basic principles of sound graphical design and applies these principles to examples using the graphical functions available in R. It offers a wide array of graphical displays for the presentation of data, including modern tools for data visualization and representation.
Features • Emphasizes the fundamentals of statistical graphics • Describes the strengths and weaknesses of each graphical display in R • Presents technical theoretical details on topics such as the estimation of quantiles, kernel density estimation, locally weighted polynomial regression, and splines • Includes engaging examples of real-world data, end-of-chapter exercises, and many illustrations, with some in color • Provides downloadable R code and data for the figures in the text on the book’s website
StatLab, Heidelberg, Germany
—Journal of Statistical Software, December 2009
“… it is the integration of interesting examples and associated R code that make the text a pleasure to read and work through. The examples are neither overly trivial … nor excessively complicated, and the R code is similarly accessible without being either too simple or complex. …” —Ronald D. Fricker, Jr., The American Statistician
Using a range of examples, this introduction shows students how R can be employed to tackle statistical problems. A handy appendix includes a collection of R language elements and functions, serving as a quick reference and starting point to access the rich information that comes bundled with R. Helping students become familiar with R, the author offers the full R source code for all examples, selected solutions, and other material on the book’s website.
Contents Basic Data Analysis. Regression. Comparisons. Dimensions 1, 2, 3, …, Infinity. R as a Programming Language and Environment. References. Functions and Variables by Topic. Function and Variable Index. Subject Index. Catalog no. C6782, 2009, 264 pp. ISBN: 978-1-4200-8678-2, $82.95
Contents A Single Discrete Variable. A Single Continuous Variable. Two Variables. Statistical Models for Two or More Variables. References. Index. Catalog no. C0756, 2010, 489 pp. ISBN: 978-1-58488-087-5, $69.95
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Statistics for Business & Finance
New!
Stochastic Finance A Numeraire Approach Jan Vecer Columbia University, New York, New York, USA
“… this is the first book to stress the fundamental role that numeraires play in modern asset pricing theory. The author is the leading expert on the subject so it is a pleasure to highly recommend this book.” —Peter Carr, Ph.D., Managing Director of Morgan Stanley, and Executive Director of NYU’s Masters in Math Finance
“Finally, we have a full volume with a 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. …” —Uwe Wystup, Ph.D., Managing Director of MathFinance AG
This classroom-tested text provides a deep understanding of derivative contracts. It treats price as a number of units of one asset needed for an acquisition of a unit of another asset instead of expressing prices in dollar terms exclusively. This numeraire approach leads to simpler pricing options for complex products.
Features • Focuses on fundamental principles of pricing • Shows students how to identify the basic assets of a given contract • Explains how to compute the prices of contingent claims in terms of various reference assets • Presents examples of a model independent formula for European call options; a simple method for pricing barrier options, lookback options, and Asian options; and a formula for options on LIBOR • Provides prerequisite material on probability and stochastic calculus in the appendix • Includes solutions to odd-numbered exercises at the back of the book Catalog no. K10632, January 2011, 342 pp. ISBN: 978-1-4398-1250-1, $69.95
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Contents Elements of Finance Binomial Models Diffusion Models Interest Rate Contracts Forward LIBOR Swaps and Swaptions Term Structure Models Barrier Options Types of Barrier Options Barrier Option Pricing via Power Options Price of a Down-and-In Call Option Connections with the Partial Differential Equations Lookback Options Connections of Lookbacks with Barrier Options Partial Differential Equation Approach for Lookbacks Maximum Drawdown American Options American Options on No-Arbitrage Assets American Call and Puts on Arbitrage Assets Perpetual American Put Partial Differential Equation Approach Contracts on Three or More Assets: Quantos, Rainbows and “Friends” Pricing in the Geometric Brownian Motion Model Hedging Asian Options Pricing in the Geometric Brownian Motion Model Hedging of Asian Options Reduction of the Pricing Equations Jump Models Poisson Process Geometric Poisson Process Pricing Equations European Call Option in Geometric Poisson Model Lévy Models with Multiple Jump Sizes For more complete contents, visit www.crctextbooks.com
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Statistics for Business & Finance
Applied Statistics for Business and Economics Robert M. Leekley
Stochastic Financial Models
Illinois Wesleyan University, Bloomington, USA
Douglas Kennedy
“…this text does an outstanding job of integrating things on the mathematical level. … [Students] will like the clear and to the point writing.”
“This book is a superb beginning level text for senior undergraduate/graduate mathematicians, which is based on lectures delivered by its author to many generations of appreciative Cambridge mathematicians. Many of my own Ph.D. and masters students have taken Dr. Kennedy’s course to uniformly good reviews; this readable book will make its material available to a worldwide audience. … the book contains 40 pages of fully worked out solutions … .”
—MAA Reviews, September 2010
Designed for a one-semester course, this text offers students in business and the social sciences an effective introduction to some of the most basic and powerful techniques available for understanding their world. After reading the book, students will be able to summarize data in insightful ways using charts, graphs, and summary statistics as well as make inferences from samples. Numerous interesting and important examples reflect real-life situations and calculations can be performed using any standard spreadsheet package. To help with the examples, the author offers both actual and hypothetical databases on his website. A solutions manual is available for qualifying instructors.
Contents Describing Data: Tables and Graphs. Describing Data: Summary Statistics. Basic Probability. Probability Distributions. Sampling and Sampling Distributions. Estimation and Confidence Intervals. Tests of Hypotheses: OneSample Tests. Tests of Hypotheses: Two-Sample Tests. Tests of Hypotheses: Contingency and Goodness-of-Fit. Tests of Hypotheses: ANOVA and Tests of Variances. Simple Regression and Correlation. Multiple Regression. Time-Series Analysis. Appendices. Index. Catalog no. K10296, 2010, 496 pp. ISBN: 978-1-4398-0568-8, $79.95
Trinity College, Cambridge, UK
—M.A.H. Dempster, Centre for Financial Research, Statistical Laboratory, University of Cambridge, UK
Developed from the esteemed author’s advanced undergraduate and graduate courses at the University of Cambridge, this text provides a hands-on, sound introduction to mathematical finance. It is suitable for students at different levels of mathematical maturity. Assuming no prior knowledge of stochastic calculus or measure-theoretic probability, the author includes the relevant mathematical background as well as many exercises with solutions. He takes a hands-on approach to calculations, enabling students to derive the prices of many common financial products.
Contents Portfolio Choice. The Binomial Model. A General Discrete-Time Model. Brownian Motion. The Black–Scholes Model. Interest-Rate Models. Solutions. Appendices. Further Reading. References. Index. Catalog no. C3452, 2010, 264 pp. ISBN: 978-1-4200-9345-2, $69.95
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Statistics for Engineering New!
New!
Transportation Statistics and Microsimulation
Statistical and Econometric Methods for Transportation Data Analysis
Clifford H. Spiegelman Texas A&M University, College Station, USA
Eun Sug Park
Second Edition
Laurence R. Rilett
Simon P. Washington Matthew G. Karlaftis Fred L. Mannering
University of Nebraska, Lincoln, USA
Praise for the First Edition
Texas Transportation Institute, College Station, USA
By discussing statistical concepts in the context of transportation planning and operations, Transportation Statistics and Microsimulation provides students with the necessary background for making informed transportation-related decisions. It explains the why behind standard methods and uses real-world transportation examples and problems to illustrate key concepts.
“… the definitive text on statistics in transportation for some years to come …” —Technometrics, November 2004
“… an outstanding and unique contribution to the existing transportation literature. … an excellent textbook for a number of graduate-level classes in transportation-related disciplines.” —Journal of Transportation Engineering, September/October 2004
Classroom-tested at Texas A&M University, the text covers the statistical techniques most frequently employed by transportation and pavement professionals. To familiarize students with the underlying theory and equations, it contains problems that can be solved using statistical software. The authors encourage the use of SAS’s JMP package, which enables users to interactively explore and visualize data. Students can buy their own copy of JMP at a reduced price via a postcard in the book.
With many examples and case studies, this bestselling text teaches students how to solve transportation problems using a range of analytical tools. This edition includes new chapters on logistic regression, ordered probability models, random-parameter models, and Bayesian statistical modeling. Data sets and PowerPoint and Word presentations are available online.
Contents
Contents
Overview: The Role of Statistics in Transportation Engineering. Graphical Methods for Displaying Data. Numerical Summary Measures. Probability and Random Variables. Common Probability Distributions. Sampling Distributions. Inferences: Hypothesis Testing and Interval Estimation. Other Inferential Procedures: ANOVA and Distribution-Free Tests. Inferences Concerning Categorical Data. Linear Regression. Regression Models for Count Data. Experimental Design. Cross-Validation, Jackknife, and Bootstrap Methods for Obtaining Standard Errors. Bayesian Approaches to Transportation Data Analysis. Microsimulation. Appendix.
FUNDAMENTALS: Statistical Inference I: Descriptive Statistics. Statistical Inference II: Interval Estimation, Hypothesis Testing, and Population Comparisons. CONTINUOUS DEPENDENT VARIABLE MODELS: Linear Regression. Violations of Regression Assumptions. Simultaneous-Equation Models. Panel Data Analysis. Background and Exploration in Time Series. Forecasting in Time Series: Autoregressive Integrated Moving Average (ARIMA) Models and Extensions. Latent Variable Models. Duration Models. COUNT AND DISCRETE DEPENDENT VARIABLE MODELS: Count Data Models. Logistic Regression. Discrete Outcome Models. Ordered Probability Models. Discrete/Continuous Models. OTHER STATISTICAL METHODS: Random-Parameter Models. Bayesian Models. Appendices. References. Index.
Catalog no. K10032, January 2011, 383 pp. ISBN: 978-1-4398-0023-2, $59.95
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Catalog no. C285X, January 2011, 544 pp. ISBN: 978-1-4200-8285-2, $99.95
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Biostatistics
New!
Exercises and Solutions in Biostatistical Theory Lawrence L. Kupper University of North Carolina, Chapel Hill, USA
Brian Neelon Duke University, Durham, North Carolina, USA
Sean M. O’Brien Duke University School of Medicine, Durham, North Carolina, USA
Drawn from nearly four decades of Lawrence L. Kupper’s teaching experiences as a distinguished professor in the Department of Biostatistics at the University of North Carolina, Exercises and Solutions in Biostatistical Theory presents theoretical statistical concepts, numerous exercises, and detailed solutions that span topics from basic probability to statistical inference. The text links theoretical biostatistical principles to real-world situations, including some of the authors’ own biostatistical work that has addressed complicated design and analysis issues in the health sciences. This classroom-tested material is arranged sequentially starting with a chapter on basic probability theory, followed by chapters on univariate distribution theory and multivariate distribution theory. The last two chapters on statistical inference cover estimation theory and hypothesis testing theory. Each chapter begins with an in-depth introduction that summarizes the biostatistical principles needed to help solve the exercises. Exercises range in level of difficulty from fairly basic to more challenging. By working through the exercises and detailed solutions in this book, students will develop a deep understanding of the principles of biostatistical theory. The text shows how the biostatistical theory is effectively used to address important biostatistical issues in a variety of real-world settings. Mastering the theoretical biostatistical principles described in the book will prepare students for successful study of higher-level statistical theory and will help them become better biostatisticians.
Catalog no. C7222, January 2011, 420 pp. Soft Cover, ISBN: 978-1-58488-722-5, $49.95
Contents Basic Probability Theory Univariate Distribution Theory Discrete and Continuous Random Variables Cumulative Distribution Functions Median and Mode Expectation Theory Some Important Expectations Inequalities Involving Expectations Some Important Probability Distributions for Discrete Random Variables Some Important Distributions for Continuous Random Variables Multivariate Distribution Theory Discrete and Continuous Multivariate Distributions Multivariate Cumulative Distribution Functions Expectation Theory Marginal Distributions Conditional Distributions and Expectations Mutual Independence among a Set of Random Variables Random Sample Some Important Multivariate Discrete and Continuous Probability Distributions Special Topics of Interest Estimation Theory Point Estimation of Population Parameters Data Reduction and Joint Sufficiency Methods for Evaluating the Properties of a Point Estimator Interval Estimation of Population Parameters Hypothesis Testing Theory Basic Principles Most Powerful (MP) and Uniformly Most Powerful (UMP) Tests Large-Sample ML-Based Methods for Testing a Simple Null Hypothesis versus a Composite Alternative Hypothesis Large-Sample ML-Based Methods for Testing a Composite Null Hypothesis versus a Composite Alternative Hypothesis For more complete contents, visit www.crcpress.com
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Statistics in Psychology & Social Sciences
Statistics in Psychology & Social Sciences Applied Survey Data Analysis Steven G. Heeringa, Brady T. West, and Patricia A. Berglund University of Michigan, Ann Arbor, USA
“… there is a wealth of instruction here. The writing style is expansive, keeping mathematics in check, and the material is well organized clearly into appropriate sections. I think that the book would serve any budding survey practitioner well: armed with the knowledge and practical skills covered herein, plus some real-life experience of course, one could reasonably claim to be well qualified in the subject.” —International Statistical Review (2010), 78, 3
This text provides a practical, intermediate-level statistical overview of the analysis of complex sample survey data. It emphasizes methods and worked examples using available software procedures while reinforcing the principles and theory that underlie those methods. The book contains many examples and practical exercises based on major real-world survey data sets. Although the authors use Stata for most examples in the text, they offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s website. The authors introduce a step-by-step process for approaching a survey analysis problem, present the fundamental features of complex sample designs, and show how to integrate design characteristics into statistical methods and software. They also cover novel developments in survey applications of advanced statistical techniques, including model-based analysis approaches.
Catalog no. C8066, 2010, 487 pp. ISBN: 978-1-4200-8066-7, $79.95
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Features • Demonstrates how design characteristics, such as stratification, clustering, and weighting, are easily incorporated into the statistical methods and software for survey estimation and inference • Presents many methods and models for urvey data analysis, including the linear regression, generalized linear, Cox proportional hazards, and discrete time models • Explores developments in advanced statistical techniques, such as multilevel analysis of urvey data • Supplies advice and recommendations based on the authors’ experiences as well as current thinking on best practices • Uses theory boxes to develop or explain a fundamental theoretical concept underlying statistical methods • Includes practical exercises that reinforce application of the methods • Offers software code, brief technical reports, links to example survey data sets, and more on the book’s website
Contents Applied Survey Data Analysis: Overview. Getting to Know the Complex Sample Design. Foundations and Techniques for Design-Based Estimation and Inference. Preparation for Complex Sample Survey Data Analysis. Descriptive Analysis for Continuous Variables. Categorical Data Analysis. Linear Regression Models. Logistic Regression and Generalized Linear Models for Binary Survey Variables. Generalized Linear Models for Multinomial, Ordinal, and Count Variables. Survival Analysis of Event History Survey Data. Multiple Imputation: Methods and Applications for Survey Analysts. Advanced Topics in the Analysis of Survey Data. References. Appendix.
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Statistics in Psychology & Social Sciences
Second Edition
Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences
Stanley A. Mulaik
Brian S. Everitt
Foundations of Factor Analysis Georgia Institute of Technology, Atlanta, USA
King’s College, University of London, UK
“… I must say that I am very happy that the author has taken the challenge to update and revise this precious book into the second edition. … the topics are explained clearly, and mathematics is taught as it is needed to understand a derivation of an equation or some procedure. … the book is worth having nearby … .”
“… Everitt successfully crafts a well-integrated introductory text that obviates potential difficulties by including real problems and their data sets. …”
—International Statistical Review (2010), 78
Providing a practical, thorough understanding of how factor analysis works, this textbook enables students to choose the proper factor analytic procedure, make modifications to the procedure, and produce new results. This long-awaited second edition includes a new chapter on the multivariate normal distribution, a rewritten chapter on analytic oblique rotation, and a revised chapter on confirmatory factor analysis. This edition also offers more complete coverage of descriptive factor analysis and doublet factor analysis and explores the developments of factor score indeterminacy. SPSS programs are available for download at CRC Press Online.
—Psychometrika, June 2010
“… Especially the second chapter, which shows how to look at data, is among the best I have ever seen in books on multivariate methods. … He also goes well beyond the typical graphs, showing how to explore real insights from the data. … the book is extremely easy to browse and read. … I’ll be happy to recommend this book to students and researchers.” —International Statistical Review, 2010
With many real-world examples, graphs, and exercises, this text equips students with the right statistical tools for analyzing data. The author separates mathematical details from the main text and removes the burden of performing necessary calculations by encouraging the use of R. Solutions to the problems as well as all R code and data sets for the examples are available at CRC Press Online.
Contents
Contents
Mathematical Foundations for Factor Analysis. Composite Variables and Linear Transformations. Multiple and Partial Correlations. Multivariate Normal Distribution. Fundamental Equations of Factor Analysis. Methods of Factor Extraction. Common-Factor Analysis. Other Models of Factor Analysis. Factor Rotation. Orthogonal Analytic Rotation. Oblique Analytic Rotation. Factor Scores and Factor Indeterminacy. Factorial Invariance. Confirmatory-Factor Analysis. References. Indices.
Data, Measurement, and Models. Looking at Data. Simple Linear and Locally Weighted Regression. Multiple Linear Regression. The Equivalence of Analysis of Variance and Multiple Linear Regression, and An Introduction to the Generalized Linear Model. Logistic Regression. Survival Analysis. Linear Mixed Models for Longitudinal Data. Multivariate Data and Multivariate Analysis. Principal Components Analysis. Factor Analysis. Cluster Analysis. Grouped Multivariate Data. References. Appendix. Index.
Catalog no. K10005, 2010, 548 pp. ISBN: 978-1-4200-9961-4, $82.95
Catalog no. K10396, 2010, 320 pp. Soft Cover, ISBN: 978-1-4398-0769-9, $71.95
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Environmental Statistics
Environmental and Ecological Statistics with R Song S. Qian Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R connects applied statistics to the environmental and ecological fields. It follows the general approach to solving a statistical modeling problem, covering model specification, parameter estimation, and model evaluation. The author uses many examples to illustrate the statistical models and presents R implementations of the models. The book first builds a foundation for conducting a simple data analysis task, such as exploratory data analysis and fitting linear regression models. It then focuses on statistical modeling, including linear and nonlinear models, classification and regression tree, and the generalized linear model. The text also discusses the use of simulation for model checking, provides tools for a critical assessment of the developed model, and explores multilevel regression models, which are a class of models that can have a broad impact in environmental and ecological data analysis. Based on courses taught by the author at Duke University, this textbook focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding students through the processes of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.
Features • Describes each type of statistical model through examples • Explains how to conduct data analysis • Discusses simulation for model checking, an important aspect of model development and assessment • Presents multilevel regression models, such as multilevel ANOVA, multilevel linear regression, and generalized multilevel • Shows students how the methods can be implemented using R • Offers the data sets and R scripts used in the book along with exercises and solutions on the author’s website
Contents BASIC CONCEPTS Introduction. R. Statistical Assumptions. Statistical Inference. STATISTICAL MODELING Linear Models. Nonlinear Models. Classification and Regression Tree. Generalized Linear Model. ADVANCED STATISTICAL MODELING Simulation for Model Checking and Statistical Inference. Multilevel Regression. References Index For more complete contents, visit www.crctextbooks.com
Catalog no. C6206, 2010, 440 pp., Soft Cover ISBN: 978-1-4200-6206-9, $82.95
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Statistics in Genetics & Biology
Statistics in Human Genetics and Molecular Biology Cavan Reilly University of Minnesota, Minneapolis, USA
“Very useful for those taking courses in statistics and geneticists.” —Pediatric Endocrinology Reviews, Vol. 7, No. 4, June 2010
Focusing on the roles of different segments of DNA, Statistics in Human Genetics and Molecular Biology provides students with a basic understanding of problems arising in the analysis of genetics and genomics. It presents statistical applications in genetic mapping, DNA/protein sequence alignment, and analyses of gene expression data from microarray experiments. The text introduces a diverse set of problems and a number of approaches that have been used to address these problems. It discusses basic molecular biology and likelihood-based statistics, along with physical mapping, markers, linkage analysis, parametric and nonparametric linkage, sequence alignment, and feature recognition. The text illustrates the use of methods that are widespread among researchers who analyze genomic data, such as hidden Markov models and the extreme value distribution. It also covers differential gene expression detection as well as classification and cluster analysis using gene expression data sets. With worked examples and end-of-chapter exercises, this text presents various approaches to help students solve problems at the interface of statistics, biostatistics, computer science, and related fields in applied mathematics.
Contents Basic Molecular Biology for Statistical Genetics and Genomics Basics of Likelihood-Based Statistics Markers and Physical Mapping Basic Linkage Analysis Extensions of the Basic Model for Parametric Linkage Nonparametric Linkage and Association Analysis
Catalog no. C7263, 2009, 280 pp. ISBN: 978-1-4200-7263-1, $61.95
Introduction Sib-pair method Identity by descent Affected sib-pair (ASP) methods QTL mapping in human populations A case study: dealing with heterogeneity in QTL mapping Linkage disequilibrium Association analysis Sequence Alignment Significance of Alignments and Alignment in Practice Hidden Markov Models Feature Recognition in Biopolymers Gene transcription Detection of transcription factor binding sites Computational gene recognition Multiple Alignment and Sequence Feature Discovery Dynamic programming Progressive alignment methods Hidden Markov models Block motif methods Enumeration based methods A case study: detection of conserved elements in mRNA Statistical Genomics Functional genomics The technology Spotted cDNA arrays Oligonucleotide arrays Normalization Detecting Differential Expression Multiple testing and the false discovery rate Significance analysis for microarrays Model based empirical Bayes approach A case study: normalization and differential detection Cluster Analysis in Genomics Classification in Genomics For more complete contents, visit www.crctextbooks.com
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Statistical Learning & Data Mining New!
Exploratory Data Analysis with MATLAB® Second Edition Wendy L. Martinez The Department of Defense, Fredericksburg, Virginia, USA
Angel R. Martinez Strayer University, Fredericksburg, Virginia, USA
Jeffrey L/ Solka The Department of the Navy, Dahlgren, Virginia, USA
Praise for the First Edition
Features
“… I found the book to be engagingly written, and successful in its defined task of teaching the reader to use EDA with MATLAB. I liked the graphics and thought that they fully illustrated the techniques used.”
• Shows how to use EDA methods via examples and applications
—Brian Jersky, Sonoma State University, Journal of the American Statistical Association
Read more reviews at CRC Press Online Covering innovative approaches for dimensionality reduction, clustering, and visualization, this text uses numerous examples and applications to show students how the methods are used in practice.
• Covers state-of-the-art techniques for dimensionality reduction, clustering, and visualization • Provides MATLAB code for virtually all algorithms covered in the text • Includes pseudo-code to implement algorithms in software other than MATLAB • Describes many functions of the GUI toolbox for EDA
New to the Second Edition
• Contains an eight-page color insert illustrating data output from several MATLAB examples
• Discussions of nonnegative matrix factorization, linear discriminant analysis, curvilinear component analysis, independent component analysis, and smoothing splines
Contents
• An expanded set of methods for estimating the intrinsic dimensionality of a data set • Several clustering methods, including probabilistic latent semantic analysis and spectral-based clustering • Additional visualization methods, such as a rangefinder boxplot, scatterplots with marginal histograms, biplots, and a new method called Andrews’ images • Instructions on a free MATLAB® GUI toolbox for EDA Like its predecessor, this edition continues to focus on using EDA methods, rather than theoretical aspects. The MATLAB codes for the examples, EDA toolboxes, data sets, and color versions of all figures are available for download online.
INTRODUCTION TO EXPLORATORY DATA ANALYSIS Introduction to Exploratory Data Analysis EDA AS PATTERN DISCOVERY Dimensionality Reduction - Linear Methods. Dimensionality Reduction - Nonlinear Methods. Data Tours. Finding Clusters. Model-Based Clustering. Smoothing Scatterplots. GRAPHICAL METHODS FOR EDA Visualizing Clusters. Distribution Shapes. Multivariate Visualization. Appendices References Index
Catalog no. K10616, January 2011, 530 pp. ISBN: 978-1-4398-1220-4, $89.95
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Ensure your students keep up with cutting-edge theory and applications of MCMC
• Thorough coverage of the theoretical foundations and algorithmic and computational methodology make constructing, implementing, and choosing MCMC techniques easier than ever • In-depth introductory section allows students new to MCMC to become thoroughly acquainted with the basic theory, algorithms, and applications
• Detailed examples and case studies of realistic scientific problems showcase the diversity of methods used by the wide-ranging MCMC community Visit CRC Press Online for more information and to view the complete table of contents.
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