Reducing bias without prejudicing sign

Peter Hall, Brett Presnell, Berwin A. Turlach

Research output: Contribution to journalArticle

Abstract

Jackknife and bootstrap bias corrections are based on a differencing argument which does not necessarily respect the sign of the true parameter value. Depending on sampling variability they can over-correct, producing a final estimator that is negative when one knows on physical grounds that it should be positive. To overcome this problem we suggest a simple, alternative bootstrap approach, based on biased-bootstrap methods. Our technique has similar properties to the standard uniform-bootstrap method in cases where the latter does not endanger sign, but it respects sign in a canonical way when the standard method disregards it.

Original languageEnglish
Pages (from-to)507-518
Number of pages12
JournalAnnals of the Institute of Statistical Mathematics
Volume52
Issue number3
DOIs
Publication statusPublished - 2000
Externally publishedYes

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