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A Smooth Bootstrap Procedure towards Deriving Confidence Intervals for the Relative Risk

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  • Dongliang Wang
  • Alan D. Hutson

Abstract

Given a pair of sample estimators of two independent proportions, bootstrap methods are a common strategy towards deriving the associated confidence interval for the relative risk. We develop a new smooth bootstrap procedure, which generates pseudo-samples from a continuous quantile function. Under a variety of settings, our simulation studies show that our method possesses a better or equal performance in comparison with asymptotic theory based and existing bootstrap methods, particularly for heavily unbalanced data in terms of coverage probability and power. We illustrate our procedure as applied to several published data sets.

Suggested Citation

  • Dongliang Wang & Alan D. Hutson, 2014. "A Smooth Bootstrap Procedure towards Deriving Confidence Intervals for the Relative Risk," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(9), pages 1979-1990, May.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:9:p:1979-1990
    DOI: 10.1080/03610926.2012.681418
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