图书信息

内容简介

图书目录

图书信息

书 名:自助法及其应用

作　者：（瑞士）戴维森

出版社： 世界图书出版公司

出版时间： 2010-4-1

ISBN: 9787510005510

开本： 16开

定价: 89.00元

内容简介

This series of high quality upper-division textbooks and expository monographs covers all areas of stochastic applicable mathematics. The topics range from pure and applied statistics to probability theory，operations research, mathematical programming, and optimzation. The books contain clear presentations of new developments in the field and also of the state of the art in classical methods. While emphasizing rigorous treatment of theoretical methods, the books contain important applications and discussionsof new techniques made possible be advances in computational methods.

图书目录

Preface

1 Introduction

2 The Basic Bootstraps

2.1 Introduction

2.2 Parametric Simulation

2.3 Nonparametric Simulation

2.4 Simple Confidence Intervals

2.5 Reducing 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

Further Ideas

3.1 Introduction

3.2 Several Samples

3.3 Semiparametric 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 Resampling for Parametric Tests

4.3 Nonparametric Permutation Tests

4.4 Nonparametric Bootstrap Tests

4.5 Adjusted P-values

4.6 Estimating Properties of Tests

4.7 Bibliographic Notes

4.8 Problems

4.9 Practicals

5 Confidence Intervals

5.1 Introduction

5.2 Basic Confidence Limit Methods

5.3 Percentile Methods

5.4 Theoretical Comparison of Methods

5.5 Inversion of Significance Tests

5.6 Double Bootstrap Methods

5.7 Empirical Comparison of Bootstrap Methods

5.8 Multiparameter Methods

5.9 Conditional Confidence Regions

5.10 Prediction

5.11 Bibliographic Notes

5.12 Problems

5.13 Practicals

6 Linear 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 Practicals

7 Farther Topics in Regression

7.1 Introduction

7.2 Generalized Linear Models

7.3 Survival Data

7.4 Other Nonlinear Models

7.5 Misclassification 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 Point Processes

8.4 Bibliographic Notes

8.5 Problems

8.6 Practicals

9 Improved Calculation

9.1 Introduction

9.2 Balanced Bootstraps

9.3 Control Methods

9.4 Importance Resampling

9.5 Saddlepoint Approximation

9.6 Bibliographic Notes

9.7 Problems

9.8 Practicals

10 Semiparametric Likelihood Inference

10.1 Likelihood

10.2 Multinomial-Based Likelihoods

10.3 Bootstrap Likelihood

10.4 Likelihood Based on Confidence Sets

10.5 Bayesian Bootstraps

10.6 Bibliographic Notes

10.7 Problems

10.8 Practicala

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. Cumulant Calculations

Bibliography

Name Index

Example index

Subject index