Bootstrapping A Nonparametric Approach to Statistical Inference
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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.
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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
List price: $22.00
Copyright year: 1993
Publisher: SAGE Publications, Incorporated
Publication date: 8/9/1993
Size: 5.50" wide x 8.35" long x 0.25" tall
|Traditional Parametric Statistical Inference|
|Bootstrap Statistical Inference|
|Bootstrapping a Regression Model|
|Monte Carlo Evaluation of the Bootstrap|
|Statistical Inference Using the Bootstrap|
|Bootstrap Confidence Intervals|
|Applications of Bootstrap Confidence Intervals|
|Confidence Intervals for Statistics With Unknown Sampling Distributions|
|Inference When Traditional Distributional Assumptions Are Violated|
|Limitations of the Bootstrap|