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Bootstrapping : A Nonparametric Approach to Statistical Inference - Christopher Z. Mooney

Bootstrapping

A Nonparametric Approach to Statistical Inference

Paperback Published: 9th August 1993
ISBN: 9780803953819
Number Of Pages: 80

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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

Introduction
Traditional Parametric Statistical Inference
Bootstrap Statistical Inference
Bootstrapping a Regression Model
Theoretical Justification
The Jackknife
Monte Carlo Evaluation of the Bootstrap
Statistical Inference Using the Bootstrap
Bias Estimation
Bootstrap Confidence Intervals
Applications of Bootstrap Confidence Intervals
Confidence Intervals for Statistics With Unknown Sampling Distributions
Inference When Traditional Distributional Assumptions Are Violated
Conclusion
Future Work
Limitations of the Bootstrap
Concluding Remarks
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9780803953819
ISBN-10: 080395381X
Series: Quantitative Applications in the Social Sciences
Audience: Professional
Format: Paperback
Language: English
Number Of Pages: 80
Published: 9th August 1993
Country of Publication: US
Dimensions (cm): 14.0 x 21.7  x 0.5
Weight (kg): 0.12