Computational Statistics

This new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing.  The book is comprised of four main parts spanning the field:

• Optimization
• Integration and Simulation
• Bootstrapping
• Density Estimation and Smoothing

Within these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods.  The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data.  The book website now includes comprehensive R code for the entire book.  There are extensive exercises, real examples, and helpful insights about how to use the methods in practice.

GEOF H. GIVENS, PhD, is Associate Professor in the Department of Statistics at Colorado State University. He serves as Associate Editor for Computational Statistics and Data Analysis. His research interests include statistical problems in wildlife conservation biology including ecology, population modeling and management, and automated computer face recognition.

JENNIFER A. HOETING, PhD, is Professor in the Department of Statistics at Colorado State University. She is an award-winning teacher who co-leads large research efforts for the National Science Foundation. She has served as associate editor for the Journal of the American Statistical Association and Environmetrics. Her research interests include spatial statistics, Bayesian methods, and model selection.

Givens and Hoeting have taught graduate courses on computational statistics for nearly twenty years, and short courses to leading statisticians and scientists around the world.

A valuable new edition of the complete guide to modern statistical computing Computational Statistics, Second Edition continues to serve as a comprehensive guide to the theory and practice of statistical computing. Like its predecessor, the new edition spans a broad range of modern and classic topics including optimization, integration, Monte Carlo methods, bootstrapping, density estimation and smoothing. Algorithms are explained both conceptually and by using step-by-step descriptions, and are illustrated with detailed examples and exercises. Important features of this Second Edition include: Examples based on real-world applications from various fields including genetics, ecology, economics, network systems, biology, and medicine Explanations of how computational methods are important components of major statistical approaches such as Bayesian models, linear and generalized linear models, random effects models, survival models, and hidden Markov models Expanded coverage of Markov chain Monte Carlo methods New topics such as sequential sampling methods, particle filters, derivative free optimization, bootstrapping dependent data, and adaptive MCMC New exercises and examples that help readers develop the skills needed to apply computational methods to a broad array of statistical problems A companion website offering datasets and code in the R software package Computational Statistics, Second Edition is perfect for advanced undergraduate or graduate courses in statistical computing and as a reference for practicing statisticians.

A valuable new edition of the complete guide to modern statistical computing Computational Statistics, Second Edition continues to serve as a comprehensive guide to the theory and practice of statistical computing. Like its predecessor, the new edition spans a broad range of modern and classic topics including optimization, integration, Monte Carlo methods, bootstrapping, density estimation and smoothing. Algorithms are explained both conceptually and by using step-by-step descriptions, and are illustrated with detailed examples and exercises. Important features of this Second Edition include: Examples based on real-world applications from various fields including genetics, ecology, economics, network systems, biology, and medicine Explanations of how computational methods are important components of major statistical approaches such as Bayesian models, linear and generalized linear models, random effects models, survival models, and hidden Markov models Expanded coverage of Markov chain Monte Carlo methods New topics such as sequential sampling methods, particle filters, derivative free optimization, bootstrapping dependent data, and adaptive MCMC New exercises and examples that help readers develop the skills needed to apply computational methods to a broad array of statistical problems A companion website offering datasets and code in the R software package Computational Statistics, Second Edition is perfect for advanced undergraduate or graduate courses in statistical computing and as a reference for practicing statisticians.

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