An Introduction to the Bootstrap
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
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This is a very good introduction to boostrapping. Major techniques are clearly explained with pros and cons for their use. The reader has enough information to choose which solution to perform depending on his data. Some more advanced sections are clearly labelled as such. Basic statistical concepts are explained in the light of bootstrap, which is very useful.
Unfortunately it is now more than 15 years old, and that's a lot in the history of bootstrap. A refresh with new techniques would certainly be useful. A second issue is the examples. They are dramatically scarce and under-detailed.
Nonetheless it is a worthwhile reading if you want to begin with bootstrap.
The accuracy of a sample mean
Random samples and probabilities
The empirical distribution function and the plugin
Standard errors and estimated standard errors
The bootstrap estimate of standard error
More complicated data structures
Crossvalidation and other estimates of prediction
Adaptive estimation and calibration
Assessing the error in bootstrap estimates
A geometrical representation for the bootstrap
An overview of nonparametric and parametric
Further topics in bootstrap confidence intervals
Efficient bootstrap computations
Estimates of bias
Confidence intervals based on bootstrap tables
Confidence intervals based on bootstrap
Better bootstrap confidence intervals
Hypothesis testing with the bootstrap