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＜図書＞
Bootstrap methods : a guide for practitioners and researchers

責任表示 Michael R. Chernick
シリーズ Wiley series in probability and mathematical statistics
データ種別 図書
版 2nd ed
出版情報 Hoboken, N.J. : John Wiley & Sons , c2008
本文言語 英語
大きさ xviii, 369 p. : ill. ; 25 cm
概要 Over the past decade, the application of bootstrap methods to new areas of study has expanded, resulting in theoretical and applied advances across various fields. Bootstrap Methods, Second Edition is...a highly approachable guide to the multidisciplinary, real-world uses of bootstrapping and is ideal for readers who have a professional interest in its methods, but are without an advanced background in mathematics.続きを見る
目次 1. What is bootstrapping?
1.1. Background
1.2. Introduction
1.3. Wide range of applications
1.4. Historical notes
1.5. Summary
2. Estimation
2.1. Estimating bias
2.1.1. How to do it by bootstrapping
2.1.2. Error rate estimation in discrimination
2.1.3. Error rate estimation: an illustrative problem
2.1.4. Efron's patch data example
2.2. Estimating location and dispersion
2.2.1. Means and medians
2.2.2. Standard errors and quartiles
2.3. Historical notes
3. Confidence sets and hypothesis testing
3.1. Confidence sets
3.1.1. Typical value theorems for M-estimates
3.1.2. Percentile method
3.1.3. Bias Correction and the Acceleration Constant
3.1.4. Iterated Bootstrap
3.1.5. Bootstrap Percentile t Confidence Intervals
3.2. Relationship Between Confidence Intervals and Tests of Hypotheses
3.3. Hypothesis Testing Problems
3.3.1. Tendril DX Lead Clinical Trial Analysis
3.4. Application of bootstrap confidence Intervals to Binary Dose-Response Modeling
3.5. Historical Notes
4. Regression analysis
4.1. Linear Models
4.1.1. Gauss-Markov Theory
4.1.2. Why Not Just Use Least Squares?
4.1.3. Should I Bootstrap the Residuals from the Fit?
4.2. Nonlinear Models
4.2.1. Examples of Nonlinear Models
4.2.2. Quasi-optical Experiment
4.3. Nonparametric Models
4.4. Historical Notes
5. Forecasting and Time Series Analysis
5.1. Methods of Forecasting
5.2. Time Series Models
5.3. When Does Bootstrapping Help with Prediction Intervals?
5.4. Model-Based Versus Block Resampling
5.5. Explosive Autoregressive Processes
5.6. Bootstrapping-Stationary Arma Models
5.7. Frequency-Based Approaches
5.8. Sieve Bootstrap
5.9. Historical notes
6. Which resampling Method Should You Use?
6.1. Related Methods
6.1.1. Jackknife
6.1.2. Delta Method, Infinitesimal Jackknife, and Influence Functions
6.1.3. Cross-Validation
6.1.4. Subsampling
6.2. Bootstrap variants
6.2.1. Bayesian bootstrap
6.2.2. Smoothed bootstrap
6.2.3. Parametric Bootstrap
6.2.4. Double bootstrap
6.2.5. m-out-of-n Bootstrap
7. Efficient and Effective Simulation
7.1. How Many Replications?
7.2. Variance Reduction Methods
7.2.1. Linear Approximation
7.2.2. Balanced Resampling
7.2.3. Antithetic Variates
7.2.4. Importance Sampling
7.2.5. Centering
7.3. When Can Monte Carlo Be Avoided?
7.4. Historical Notes
8. Special Topics
8.1. Spatial Data
8.1.1. Kriging
8.1.2. Block Bootstrap on Regular Grids
8.1.3. Block Bootstrap on Irregular Grids
8.2. Subset Selection
8.3. Determining the Number of Distributions in a Mixture Model
8.4. Censored Data
8.5. p-Value Adjustment
8.5.1. Description of Westfall-Young Approach
8.5.2. Passive Plus OX Example
8.5.3. Consulting Example
8.6. Bioequivalence Applications
8.6.1. Individual Bioequivalence
8.6.2. Population Bioequivalence
8.7. Process Capability Indices
8.8. Missing Data
8.9. Point Processes
8.10. Lattice Variables
8.11. Historical Notes
9. When Bootstrapping Fails Along with Remedies for Failures
9.1. Too Small of a Sample Size
9.2. Distributions with Infinite Moments
9.2.1. Introduction
9.2.2. Example of Inconsistency
9.2.3. Remedies
9.3. Estimating Extreme Values
9.3.1. Introduction
9.3.2. Example of Inconsistency
9.3.3. Remedies
9.4. Survey Sampling
9.4.1. Introduction
9.4.2. Example of Inconsistency
9.4.3. Remedies
9.5. Data Sequences that Are M-Dependent
9.5.1. Introduction
9.5.2. Example of Inconsistency When Independence Is Assumed
9.5.3. Remedies
9.6. Unstable Autoregressive Processes
9.6.1. Introduction
9.6.2. Example of Inconsistency
9.6.3. Remedies
9.7. Long-Range Dependence
9.7.1. Introduction
9.7.2. Example of Inconsistency
9.7.3. Remedies
9.8. Bootstrap Diagnostics
9.9. Historical Notes.続きを見る

所蔵情報


: [hard] 理系図3F 数理独自 CHER/35/1a 2008
023212007006962

書誌詳細

一般注記 "Wiley-Interscience."
Includes bibliographical references (p. 188-329) and indexes
著者標目 *Chernick, Michael R.
件　名 LCSH:Bootstrap (Statistics)
分　類 LCC:QA276.8
DC22:519.5/44
書誌ID 1001241242
ISBN 9780471756217
NCID BA84300168
巻冊次 : [hard] ; ISBN:9780471756217
登録日 2009.09.18
更新日 2017.10.03

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