Bootstrapping: A Nonparametric Approach to Statistical Inference - Brossura

Duval, Robert D.; Mooney, Christopher Z.

Brossura

ISBN 10: 080395381X ISBN 13: 9780803953819

Casa editrice: SAGE Publications, Inc, 1993

Riassunto

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, percenti

Su questo libro

L'autore

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

Contenuti

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

Dettagli dell?edizione

• Editore: SAGE Publications, Inc
• Data di pubblicazione: 1993
• ISBN 10: 080395381X
• ISBN 13: 9780803953819
• Rilegatura: Copertina flessibile
• Lingua: Inglese
• Numero edizione: 1
• Numero di pagine: 80