As the book fxpanded，we realized that a fully comprehensive treatment wasbeyond US,and that certain topics could be given only a cursory treatment becausetoo little is known about them.So it is that the reader will find only brief accountsof bootstrap methods for hierarchical data,missing data problems。model selection，robust estimation,nonparametric regression，and complex data.But we do try topoint the more ambitious reader in the fight direction.

Preface

1 Introduction

2 The Basic Bootstraps

 2.1 Introduction

 2.2 Parametric Simulation

 2.3 Nonparametric Simulation

 2.4 Simple Contidence Intervais

 2.5 Redudng Error

 2.6 Statistical Issues

 2.7 Nonparametric Approximations for Variance and Bias

 2.8 Subsampling Methods

 2.9 Bibliographic Notes

 2.10 Problems

 2.11 Practicals

3 Further Idess

 3.1 Introduction

 3.2 Several Samples

 3.3 Sereiparametric Models

 3.4 Smooth Estimates of F

 3.5 Censoring

 3.6 Missing Data

 3.7 Finite Population Sampling

 3.8 Hierarchical Data

 3.9 Bootstrapping the Bootstrap

 3.10 Bootstrap Diagnostics

 3.11 Choice of Estimator from the Data

 3.12 Bibliographic Notes

 3.13 Problems

 3.14 Practicals

4 Tests

 4.1 Introduction

 4.2 Resampfing for Parametric TEsts

 4.3 Nonparametric Permutafion Tests

 4.4 Nonparametric Bootstrap Tests

 4.5 Adjusted P-valnes

 4.6 Estimating Properties of Tests

 4.7 Bibliographic Notes

 4.8 Problems

 4.9 Practicals

5 Confidence latervals

 5.1 Introduction

 5.2 Basic Confidenee Limit Methods

 5.3 Percentile Methods

 5.4 Theorotical Comparison of Methods

 5.5 Inversion of Significance Tcsts

 5.6 Doublc Bootstrap Methods

 5.7 Empirical Comparison of Boot~rap Method

 5.8 Multiparameter Methods

 5.9 Conditional Confidence Regions

 5.10 Prediction

 5.11 Bibliographic Notes

 5.12 problems

 5.13 Practicals

6 Liaear Regression

 6.1 Introduction

 6.2 Least Squares Linear Regression

 6.3 Multiple Linear Regression

 6.4 Aggregate Prediction Error and Variable Selection

 6.5 Robust Regression

 6.6 Bibliographic Notes

 6.7 Problems

 6.8 Pracdcals

7 Farther Topics in Regression

 7.1 Introduction

 7.2 Generalized Linear Modds

 7.3 Survival Data

 7.4 Other Nonlinear Models

 7.5 Misclassifieation Error

 7.6 Nonparametric Regression

 7.7 Bibliographic Notes

 7.8 Problems

 7.9 Practicals

8 Complex Dependence

 8.1 Introduction

 8.2 Time Series

 8.3 PointProcesses

 8.4 Bibliographic Notes

 8.5 Problems

 8.6 Practicals

9 Improved Calculation

 9.1 Introduction

 9.2 Balanced Bootaraps

 9.3 ControI Methods

 9.4 Importance Resanling

 9.5 Saddlepoint Approximation

 9.6 Bibliographic Notes

 9.7 Problems

 9.8 Practicals

10 Semiparametrie Likelihood Inference

 10.1 LiIcelihood

 10.2 Multinoimal-Based Likelihoods

 10.3 Bootstrap Likelihood

 10.4 Likelihood Based on Confidence Sets

 10.5 Bayesian Bootstraps

 10.6 Bibfiographic Notes

 10.7 Problems

 10.8 Practicals

11 Computer Implementation

 11.1 Introduction

 11.2 Basic Bootstraps

 11.3 Further Ideas

 11.4 Tests

 11.5 Confidence Intervals

 11.6 Linear Regression

 11.7 Further Topics in Regression

 11.8 Time Series

 11.9 Improved Simulation

 11.10 Semiparametric Likelihoods

Appendix A.Cumulaat Calcalatioas

Bibliography

NameIndex

Exampleindex

Subject index