Bootstrapping

A Nonparametric Approach to Statistical Inference

AvChristopher Z. Mooney,Robert D. Duval

Häftad, Engelska, 1993

Del 95 i serien Quantitative Applications in the Social Sciences

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Beskrivning

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.

Produktinformation

Utgivningsdatum:1993-09-29
Mått:139 x 215 x 4 mm
Vikt:120 g
Format:Häftad
Språk:Engelska
Serie:Quantitative Applications in the Social Sciences
Antal sidor:80
Upplaga:1
Förlag:SAGE Publications
ISBN:9780803953819

Utforska kategorier

Sociologi inom Samhälle och politik

Mer om författaren

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

Innehållsförteckning

PART ONE: INTRODUCTIONTraditional Parametric Statistical InferenceBootstrap Statistical InferenceBootstrapping a Regression ModelTheoretical JustificationThe JackknifeMonte Carlo Evaluation of the BootstrapPART TWO: STATISTICAL INFERENCE USING THE BOOTSTRAPBias EstimationBootstrap Confidence IntervalsPART THREE: APPLICATIONS OF BOOTSTRAP CONFIDENCE INTERVALSConfidence Intervals for Statistics With Unknown Sampling DistributionsInference When Traditional Distributional Assumptions Are ViolatedPART FOUR: CONCLUSIONFuture WorkLimitations of the BootstrapConcluding Remarks