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Bootstrapping: A Nonparametric Approach to Statistical Inference
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Comment from the Stata technical group
Bootstrapping: A Nonparametric Approach to Statistical Inference, by C. Z. Mooney and R. D. Duval, provides one of the best introductions to the bootstrap you are likely to encounter. Although it was written for social science researchers, anyone familiar with classical statistical procedures will also find this text useful. Included are discussions of bias and variance estimates, confidence intervals, and statistical inference. The authors also discuss results from Monte Carlo simulations, empirically reassuring the reader that the bootstrap works as advertised.
Table of contents
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Series Editor’s Introduction
Traditional Parametric Statistical Inference
Bootstrap Statistical Inference
Bootstrapping a Regression Model
Monte Carlo Evaluation of the Bootstrap
2. Statistical Inference Using the Bootstrap
Bootstrap Confidence Intervals
3. Applications of Bootstrap Confidence Intervals
Confidence Intervals for Statistics With Unknown Sampling Distributions
The Sample Mean From a Small SampleInference When Traditional Distributional Assumptions Are Violated
The Difference Between Two Sample Medians
OLS Regression With a Nonnormal Error Structure
Limitations of the Bootstrap
Appendix: Bootstrapping With Statistical Software Packages
About the Authors
Classroom and web training
Teaching with Stata
Statalist: The Stata Forum
Last updated: 16 November 2022
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