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Bootstrapping: A Nonparametric Approach to Statistical Inference
Comment from the Stata technical groupBootstrapping: 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 contentsSeries Editor’s Introduction
Acknowledgments
1. Introduction
Traditional Parametric Statistical Inference
Bootstrap Statistical Inference Bootstrapping a Regression Model Theoretical Justification The Jackknife Monte Carlo Evaluation of the Bootstrap 2. Statistical Inference Using the Bootstrap
Bias Estimation
Bootstrap Confidence Intervals 3. Applications of Bootstrap Confidence Intervals
Confidence Intervals for Statistics With Unknown Sampling Distributions
The Sample Mean From a Small Sample
Inference When Traditional Distributional Assumptions Are Violated
The Difference Between Two Sample Medians
OLS Regression With a Nonnormal Error Structure
4. Conclusion
Future Work
Limitations of the Bootstrap Concluding Remarks Appendix: Bootstrapping With Statistical Software Packages
Notes
References
About the Authors
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