Bootstrapping
A Nonparametric Approach to Statistical Inference
1st Edition
Published on 29. September 1993
Book
Paperback/Softback
80 pages
978-0-8039-5381-9 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Thousand Oaks
United States
Target group
Professional and scholarly
Dimensions
Height: 216 mm
Width: 140 mm
Thickness: 5 mm
Weight
114 gr
ISBN-13
978-0-8039-5381-9 (9780803953819)
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Schweitzer Classification
Persons
Christopher Z. Mooney is a professor of political studies with a joint appointment in the Institute of Government and Public Affairs.
Mooney studies U.S. state politics and policy, with special focus on legislative decision making, morality policy, and legislative term limits.
He is the founding editor of State Politics and Policy Quarterly, the premier academic journal in its field and has published dozens of articles and books, including Lobbying Illinois - How You Can Make a Difference in Public Policy.
Prior to arriving at UIS in 1999, he taught at West Virginia University and the University of Essex in the United Kingdom
Mooney studies U.S. state politics and policy, with special focus on legislative decision making, morality policy, and legislative term limits.
He is the founding editor of State Politics and Policy Quarterly, the premier academic journal in its field and has published dozens of articles and books, including Lobbying Illinois - How You Can Make a Difference in Public Policy.
Prior to arriving at UIS in 1999, he taught at West Virginia University and the University of Essex in the United Kingdom
Content
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
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