Computational Statistics

Retaining the general organization and style of its predecessor, this new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing and computational statistics. 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. 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.