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Bootstrapping
A Nonparametric Approach to Statistical Inference



September 1993 | 80 pages | SAGE Publications, Inc
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.
 
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

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ISBN: 9780803953819
£22.99

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