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Bootstrap Confidence Intervals and Bootstrap Approximations

Thomas Jules DiCiccioStanford University. Department of Statistics

出版	Stanford University, Department of Statistics, 1986
URL	http://books.google.com.hk/books?id=hy96GwAACAAJ&hl=&source=gbs_api

註釋

This document studies the BC sub a bootstrap procedure for constructing parametric and non-parametric confidence intervals. The BC sub a interval relies on the existence of a transformation that maps the problem into a normal scaled transformation family. The authors show how to construct this transformation in general. Exploiting this, they derive an interval that equals the BC sub a interval to second order, computable without bootstrap sampling. As a further benefit, this construction provides a second order correct approximation to the bootstrap distribution of a statistic, computed without bootstrap sampling. Both the new interval and the approximation require only n+2 evaluations of the statistic, where n is the sample size. (Author).