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Computational Statistics, Hardcover by Givens, Geof H.; Hoeting, Jennifer A.,...
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eBay item number:354952681132
Item specifics
- Condition
- Book Title
- Computational Statistics
- ISBN
- 9780470533314
- Subject Area
- Mathematics, Science
- Publication Name
- Computational Statistics
- Publisher
- Wiley & Sons, Incorporated, John
- Item Length
- 9.2 in
- Subject
- Probability & Statistics / Multivariate Analysis, Probability & Statistics / General, Graphic Methods, Physics / Mathematical & Computational
- Publication Year
- 2012
- Series
- Wiley Series in Computational Statistics Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 0.9 in
- Item Weight
- 28.9 Oz
- Item Width
- 6.3 in
- Number of Pages
- 496 Pages
About this product
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0470533315
ISBN-13
9780470533314
eBay Product ID (ePID)
30759299
Product Key Features
Number of Pages
496 Pages
Publication Name
Computational Statistics
Language
English
Publication Year
2012
Subject
Probability & Statistics / Multivariate Analysis, Probability & Statistics / General, Graphic Methods, Physics / Mathematical & Computational
Type
Textbook
Subject Area
Mathematics, Science
Series
Wiley Series in Computational Statistics Ser.
Format
Hardcover
Dimensions
Item Height
0.9 in
Item Weight
28.9 Oz
Item Length
9.2 in
Item Width
6.3 in
Additional Product Features
Edition Number
2
Intended Audience
Scholarly & Professional
LCCN
2012-017381
Dewey Edition
23
Series Volume Number
703
Illustrated
Yes
Dewey Decimal
519.5
Table Of Content
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
Synopsis
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., 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.
LC Classification Number
QA276.4.G58 2013
Copyright Date
2013
ebay_catalog_id
4
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