自助法及其应用

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

12 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

Wedecidedtotrytowriteabalanced account ofresamplingmethods，toincludebasic aspects of the theory which underpinned the methods，and to show as manyapplications as we could in order to illustrate the fun potential of the methods-warts and alL We quickly realized that in order for uS and others to understandand use the bootstrap，we would need suitable software，and producing it led usfurther towards a practically oriented treatment.0ur view was cemented by twofurther developments：the appearance oftwo excellent books，one bv PIeter Hallon the asymptotic theory and the other on basic methods bv Bradley Efron andRobert Tibshirani；and the chance to give further courses that included practicals.Our experience has been that hands-on computing is essential in coming to gripswith resampling ideas，so we have included practicals in this booL as well as moretheoretical problems.