自助法及其應用（簡體書）

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.本書是《劍橋概率和統計》系列之一，全面講述自助法及其應用。自助法是一種統計分析的計算機模擬計算方法，運用模擬的方法計算標準差、置信區間和顯著性檢驗。本方法不僅被統計人員大量使用，而且在生統、醫療科學、社會科學和商業中也有廣泛的應用。這種方法適用於不同深度的模型，在全參、半參和非參分析中也都適用。書中大量的應用實例使得自助法形象生動，內容豐富。一般概念和基本理論介紹地較為詳盡，數學理論一帶而過，但相關的必須理論知識並不顯得欠缺。每章末都附有理論和實踐的練習。目次：導論；基本自助法；深層次思想；檢驗；置信區間；線性回歸；回歸中的高級話題；複雜依賴；計算；半參似然推理；計算機工具。
讀者對象：本書適用於具有統計基本知識所有相關人士。

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