标题：自助法及其应用
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