Geof H. Givens, Jennifer A. Hoeting

Computational Statistics (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 496
• Erscheinungstermin: 8. November 2012
• Englisch
• ISBN-13: 9781118555286
• Artikelnr.: 37355353

Autorenporträt

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.

Inhaltsangabe

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 SIMULATION AND MONTE CARLO
INTEGRATION 151 6.1 Introduction to the Monte Carlo Method 151 6.2 Exact
Simulation 152 6.3 Approximate Simulation 163 6.4 Variance Reduction
Techniques 180 Problems 195 7 MARKOV CHAIN MONTE CARLO 201 7.1
Metropolis-Hastings Algorithm 202 7.2 Gibbs Sampling 209 7.3 Implementation
218 Problems 230 8 ADVANCED TOPICS IN MCMC 237 8.1 Adaptive MCMC 237 8.2
Reversible Jump MCMC 250 8.3 Auxiliary Variable Methods 256 8.4 Other
Metropolis-Hastings Algorithms 260 8.5 Perfect Sampling 264 8.6 Markov
Chain Maximum Likelihood 268 8.7 Example: MCMC for Markov Random Fields 269
Problems 279 PART III BOOTSTRAPPING 9 BOOTSTRAPPING 287 9.1 The Bootstrap
Principle 287 9.2 Basic Methods 288 9.3 Bootstrap Inference 292 9.4
Reducing Monte Carlo Error 302 9.5 Bootstrapping Dependent Data 303 9.6
Bootstrap Performance 315 9.7 Other Uses of the Bootstrap 316 9.8
Permutation Tests 317 Problems 319 PART IV DENSITY ESTIMATION AND SMOOTHING
10 NONPARAMETRIC DENSITY ESTIMATION 325 10.1 Measures of Performance 326
10.2 Kernel Density Estimation 327 10.3 Nonkernel Methods 341 10.4
Multivariate Methods 345 Problems 359 11 BIVARIATE SMOOTHING 363 11.1
Predictor-Response Data 363 11.2 Linear Smoothers 365 11.3 Comparison of
Linear Smoothers 377 11.4 Nonlinear Smoothers 379 11.5 Confidence Bands 384
11.6 General Bivariate Data 388 Problems 389 12 MULTIVARIATE SMOOTHING 393
12.1 Predictor-Response Data 393 12.2 General Multivariate Data 413
Problems 416 DATA ACKNOWLEDGMENTS 421 REFERENCES 423 INDEX 457