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Informationen zum Autor GEOF H. GIVENS, PhD, is Associate Professor in the Department of Statistics at Colorado State University. He serves as Associate Editor for Computational Statistics and Data Analysis. His research interests include statistical problems in wildlife conservation biology including ecology, population modeling and management, and automated computer face recognition. JENNIFER A. HOETING, PhD, is Professor in the Department of Statistics at Colorado State University. She is an award-winning teacher who co-leads large research efforts for the National Science Foundation. She has served as associate editor for the Journal of the American Statistical Association and Environmetrics. Her research interests include spatial statistics, Bayesian methods, and model selection. Givens and Hoeting have taught graduate courses on computational statistics for nearly twenty years, and short courses to leading statisticians and scientists around the world. Klappentext 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 SmoothingWithin 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. Zusammenfassung 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 SmoothingWithin 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. Inhaltsverzeichnis PREFACE xv ACKNOWLEDGMENTS xvii 1 REVIEW 1 1.1 Mathematical Notation 1 1.2 Taylor's Theorem and Mathematical Limit Theory 2 1.3 Statistical Notation and Probability Distributions 4 1.4 Likelihood Inference 9 1.5 Bayesian Inference 11 1.6 Statistical Limit Theory 13 1.7 Markov Chains 14 1.8 Computing 17 PART I OPTIMIZATION 2 OPTIMIZATION AND SOLVING NONLINEAR EQUATIONS 21 2.1 Univariate Problems 22 2.2 Multivariate Problems 34 Problems 54 3 COMBINATORIAL OPTIMIZATION 59 3.1 Hard Problems and NP-Completeness 59 3.2 Local Search 65 3.3 Simulated Annealing 68 3.4 Genetic Algorithms 75 3.5 Tabu Algorithms 85 Problems 92 4 EM OPTIMIZATION METHODS 97 4.1 Missing Data, Marginalization, and Notation 97 4.2 The EM Algorithm 98 4.3 EM Variants 111 Problems 121 PART II INTEGRATION AND SIMULATION 5 NUMERICAL INTEGRATION 129 5.1 Newton-Côtes Quadrature 129 5.2 Romberg Integration 139 5.3 Gaussian Quadrature 142 5.4 Frequently Encountered Problems 146 Problems 148 6 S...
