Creating non-parametric bootstrap samples using Poisson frequencies.
This article describes how, in the high-level software packages used by non-statisticians, approximate non-parametric bootstrap samples can be created and analyzed without physically creating new data sets, or resorting to complex programming. The comparable performance of this shortcut method, which uses Poisson rather than multinomial frequencies for the numbers of copies of each observation, is demonstrated theoretically by evaluating the bootstrap variance in an example where the classic estimator of the sampling variance of the statistic of interest has a known closed form. For sample sizes of 50 or more, bootstrap standard errors obtained by this shortcut method exceeded those obtained by the standard version by less than 1%. The proposed method is also evaluated in two worked examples, involving statistics whose sampling distribution is more complex. The second of these is also used to illustrate when one can and cannot use non-parametric bootstrap samples.
Hanley
James A
JA
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada. James.Hanley@McGill.CA
MacGibbon
Brenda
B
D019295:Computational Biology / Q000379:methods*; D016027:Heart Transplantation / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D025341:Principal Component Analysis; D011336:Probability; D011381:Programming Languages; D012984:Software