Computational Statistics

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9780470533314: Computational Statistics
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This new edition continues to serve as a comprehensive guide tomodern and classical methods of statistical computing.  Thebook 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 comprehensiveintroduction and step-by-step implementation summaries to accompanythe explanations of key methods.  The new edition includesupdated coverage and existing topics as well as new topics such asadaptive MCMC and bootstrapping for correlated data.  The bookwebsite now includes comprehensive R code for the entirebook.  There are extensive exercises, real examples, andhelpful insights about how to use the methods in practice.

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From the Back Cover:

A valuable new edition of the complete guide to modernstatistical computing

Computational Statistics, Second Edition continues toserve as a comprehensive guide to the theory and practice ofstatistical computing. Like its predecessor, the new edition spansa broad range of modern and classic topics including optimization,integration, Monte Carlo methods, bootstrapping, density estimationand smoothing. Algorithms are explained both conceptually and byusing step-by-step descriptions, and are illustrated with detailedexamples and exercises.

Important features of this Second Edition include:

  • Examples based on real-world applications from various fieldsincluding genetics, ecology, economics, network systems, biology,and medicine
  • Explanations of how computational methods are importantcomponents 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, particlefilters, derivative free optimization, bootstrapping dependentdata, and adaptive MCMC
  • New exercises and examples that help readers develop the skillsneeded to apply computational methods to a broad array ofstatistical problems
  • A companion website offering datasets and code in the Rsoftware package

Computational Statistics, Second Edition is perfect foradvanced undergraduate or graduate courses in statistical computingand as a reference for practicing statisticians.

About the Author:

GEOF H. GIVENS, PhD, is Associate Professor in theDepartment of Statistics at Colorado State University. He serves asAssociate Editor for Computational Statistics and DataAnalysis. His research interests include statistical problemsin wildlife conservation biology including ecology, populationmodeling and management, and automated computer facerecognition.

JENNIFER A. HOETING, PhD, is Professor in the Departmentof Statistics at Colorado State University. She is an award-winningteacher who co-leads large research efforts for the NationalScience Foundation. She has served as associate editor for theJournal of the American Statistical Association andEnvironmetrics. Her research interests include spatialstatistics, Bayesian methods, and model selection.

Givens and Hoeting have taught graduate courses on computationalstatistics for nearly twenty years, and short courses to leadingstatisticians and scientists around the world.

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Book Description John Wiley and Sons Ltd, United States, 2012. Hardback. Condition: New. 2nd Edition. Language: English. Brand new Book. 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. Seller Inventory # AAH9780470533314

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Book Description John Wiley and Sons Ltd, United States, 2012. Hardback. Condition: New. 2nd Edition. Language: English. Brand new Book. 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. Seller Inventory # AAH9780470533314

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