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 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