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:
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
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.
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:
Computational Statistics, Second Edition is perfect foradvanced undergraduate or graduate courses in statistical computingand as a reference for practicing statisticians.
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