A weighted bootstrap approach to bootstrap iteration

Peter Hall, Yoshihiko Maesono

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)137-144
    Number of pages8
    JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
    Volume62
    Issue number1
    DOIs
    Publication statusPublished - 2000

    All Science Journal Classification (ASJC) codes

    • Statistics and Probability
    • Statistics, Probability and Uncertainty

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