Computational Statistics
A valuable new edition of the complete guide to modern statistical computingComputational Statistics, Second Editioncontinues 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 thisSecond Editioninclude:Examples based on real-world applications from various fields including genetics, ecology, economics, network systems, biology, and medicineExplanations 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 modelsExpanded coverage of Markov chain Monte Carlo methodsNew topics such as sequential sampling methods, particle filters, derivative free optimization, bootstrapping dependent data, and adaptive MCMCNew exercises and examples that help readers develop the skills needed to apply computational methods to a broad array of statistical problemsA companion website offering datasets and code in the R software packageComputational Statistics, Second Editionis perfect for advanced undergraduate or graduate courses in statistical computing and as a reference for practicing statisticians.