A weighted bootstrap approach to bootstrap iteration

Abstract

The operation of resampling from a bootstrap resample, encountered in applications of the double bootstrap, may be viewed as resampling directly from the sample but using probability weights that are proportional to the numbers of times that sample values appear in the resample. This suggests an approximate approach to double-bootstrap Monte Carlo simulation, where weighted bootstrap methods are used to circumvent much of the labour involved in compounded Monte Carlo approximation. In the case of distribution estimation or, equivalently, confidence interval calibration, the new method may be used to reduce the computational labour. Moreover, the method produces the same order of magnitude of coverage error for confidence intervals, or level error for hypothesis tests, as a full application of the double bootstrap

Similar works

Full text

thumbnail-image

The Australian National University

Full text is not available
oai:openresearch-repository.anu.edu.au:1885/89094Last time updated on 4/26/2018

This paper was published in The Australian National University.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.