Computational Statistics

ISBN-10: 0470533315
ISBN-13: 9780470533314
Edition: 2nd 2013
List price: $106.00 Buy it from $58.88
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Description: 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. Approaching the topic in three major  More...

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Book details

List price: $106.00
Edition: 2nd
Copyright year: 2013
Publisher: John Wiley & Sons, Limited
Publication date: 12/7/2012
Binding: Hardcover
Pages: 496
Size: 6.50" wide x 9.75" long x 1.25" tall
Weight: 2.046
Language: English

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. Approaching the topic in three major parts—optimization, integration, and smoothing—the book includes an overview section in each chapter introduction and step-by-step implementation summaries to accompany the explanations of key methods; expanded coverage of Monte Carlo sampling and MCMC; a chapter on Alternative Viewpoints; a related Web site; new exercises; and more.

Preface
Acknowledgments
Review
Mathematical notation
Taylor's theorem and mathematical limit theory
Statistical notation and probability distributions
Likelihood inference
Bayesian inference
Statistical limit theory
Markov chains
Computing
Optimization
Optimization and Solving Nonlinear Equations
Univariate problems
Multivariate problems
Problems
Combinatorial Optimization
Hard problems and NP-completeness
Local search
Simulated annealing
Genetic algorithms
Tabu algorithms
Problems
EM Optimization Methods
Missing data, marginalization, and notation
The EM algorithm
EM Variants
Problems
Integration and Simulation
Numerical Integration
Newton-C�tes quadrature
Romberg integration
Gaussian quadrature
Frequently encountered problems
Problems
Simulation and Monte Carlo Integration
Introduction to the Monte Carlo method
Approximate Simulation
Variance reduction techniques
Problems
Markov Chain Monte Carlo
Metropolis-Hastings algorithm
Gibbs sampling
Implementation
Problems
Advanced Topics in MCMC
Adaptive MCMC
Reversible Jump MCMC
Auxiliary variable methods
Other Metropolis Hastings Algorithms
Perfect sampling
Markov chain maximum likelihood
Example: MCMC for Markov random fields
Problems
Approximating Distributions
Bootstrapping
The bootstrap principle
Basic methods
Bootstrap inference
Reducing Monte Carlo error
Bootstrapping dependent data
Bootstrap performance
Other uses of the bootstrap
Permutation tests
Problems
Density Estimation And Smoothing
Nonparametric Density Estimation
Measures of performance
Kernel density estimation
Nonkernel methods
Multivariate methods
Problems
Bivariate Smoothing
Predictor-response data
Linear smoothers
Comparison of linear smoothers
Nonlinear smoothers
Confidence bands
General bivariate data
Problems
Multivariate Smoothing
Predictor-response data
General multivariate data
Problems
Data Acknowledgments
References
Index

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