Bootstrapping : A Nonparametric Approach to Statistical Inference

Christopher Z Mooney

ISBN 10: 080395381X / ISBN 13: 9780803953819

Published by Sage Publications, Inc, 1993

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Bootstrapping, a computational nonparametric technique for `re-sampling', enables researchers to draw a conclusion about the characteristics of a population strictly from the existing sample rather than by making parametric assumptions about the estimator. Using real data examples from per capita personal income to median preference differences between legislative committee members and the entire legislature, Mooney and Duval discuss how to apply bootstrapping when the underlying sampling distribution of the statistics cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, they show the advantages and limitations of four bootstrap confidence interval methods: normal approximation, percentile, bias-corrected percentile, and percentile-t. The authors conclude with a convenient summary of how to apply this computer-intensive methodology using various available software packages.

Seller Inventory # 9780803953819

Bibliographic Details

• Title: Bootstrapping : A Nonparametric Approach to Statistical Inference
• Publisher: Sage Publications, Inc
• Publication Date: 1993
• Language: English
• ISBN 10: 080395381X
• ISBN 13: 9780803953819
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 216W x 140H x 5D millimeters 216 millimeters width by 140 millimeters height by 5 millimeters depth
• Weight: 114 grams

About this title

Synopsis

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.

About the Author

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