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.
"…an excellent book, and worth a reading by most students and practitioners in statistics… Throughout the book, the authors have spent a lot of effort in introducing difficult ideas in a simple, easy-to-understand manner…"
- Hong Kong Statistical Society Newsletter
"… written in a style that makes difficult statistical concepts easy to understand …a wonderful text for the engineer who would like to apply and understand the many different bootstrap techniques that have appeared in the literature in the last fifteen years. It makes an excellent reference text that should grace the shelves of both statisticians and non-statisticians."
- Journal of Quality Technology
The Accuracy of a Sample Mean
Random Samples and Probabilities
The Empirical Distribution Function and the Plug-In Principle
Standard Errors and Estimated Standard Errors
The Bootstrap Estimate of Standard Error
Bootstrap Standard Errors: Some Examples
More Complicated Data Structures
Estimates of Bias
Confidence Intervals Based on Bootstrap "Tables"
Confidence Intervals Based on Bootstrap Percentiles
Better Bootstrap Confidence Intervals
Hypothesis Testing with the Bootstrap
Cross-Validation and Other Estimates of Prediction Error
Adaptive Estimation and Calibration
Assessing the Error in Bootstrap Estimates
A Geometrical Representation for the Bootstrap and Jackknife
An Overview of Nonparametric and Parametric Inference
Further Topics in Bootstrap Confidence Intervals
Efficient Bootstrap Computations
Discussion and Further Topics
Appendix: Software for Bootstrap Computations