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Bootstrapping: A Nonparametric Approach to Statistical Inference Christopher Z. Mooney

Bootstrapping: A Nonparametric Approach to Statistical Inference

Christopher Z. Mooney

Published August 9th 1993
ISBN :
Kindle Edition
80 pages
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 About the Book 

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 realMoreThis 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.