Bootstrapping : a nonparametric approach to statistical inference

Christopher Z. Mooney, Robert D. Duval

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.

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  • 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

「Nielsen BookData」より


書名 Bootstrapping : a nonparametric approach to statistical inference
著作者等 Duval, Robert D.
Mooney, Christopher Z.
シリーズ名 Sage university papers series
出版元 Sage Publications
刊行年月 c1993
ページ数 vi, 73 p.
大きさ 22 cm
ISBN 080395381X
NCID BA20895633
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言語 英語
出版国 アメリカ合衆国