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Bootstrapping
A Nonparametric Approach to Statistical Inference
- Christopher Z. Mooney - University of Illinois at Chicago, USA
- Robert D. Duval - West Virginia University, USA
Volume:
95
August 1993 | 80 pages | SAGE Publications, Inc
"This book is. . . clear and well-written. . . anyone with any interest in the basis of quantitative analysis simply must read this book. . . . well-written, with a wealth of explanation. . ." --Dougal Hutchison in Educational Research Using real data examples, this volume shows how to apply bootstrapping when the underlying sampling distribution of a statistic cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, it discusses the advantages and limitations of four bootstrap confidence interval methods--normal approximation, percentile, bias-corrected percentile, and percentile-t. The book concludes with a convenient summary of how to apply this computer-intensive methodology using various available software packages.
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PART ONE: INTRODUCTION
Traditional Parametric Statistical Inference
Bootstrap Statistical Inference
Bootstrapping a Regression Model
Theoretical Justification
The Jackknife
Monte Carlo Evaluation of the Bootstrap
PART TWO: STATISTICAL INFERENCE USING THE BOOTSTRAP
Bias Estimation
Bootstrap Confidence Intervals
PART THREE: APPLICATIONS OF BOOTSTRAP CONFIDENCE INTERVALS
Confidence Intervals for Statistics With Unknown Sampling Distributions
Inference When Traditional Distributional Assumptions Are Violated
PART FOUR: CONCLUSION
Future Work
Limitations of the Bootstrap
Concluding Remarks
