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On Inconsistency of the Jackknife-after-Bootstrap Bias Estimator for Dependent Data

✍ Scribed by Soumendra Nath Lahiri

Publisher: Elsevier Science
Year: 1997
Tongue: English
Weight: 385 KB
Volume: 63
Category: Article
ISSN: 0047-259X
DOI: 10.1006/jmva.1997.1699

No coin nor oath required. For personal study only.

✦ Synopsis

B. Efron introduced jackknife-after-bootstrap as a computationally efficient method for estimating standard errors of bootstrap estimators. In a recent paper consistency of the jackknife-after-bootstrap variance estimators has been established for different bootstrap quantities for independent and dependent data. In this paper, it is shown that in the dependent case, the standard jackknife-after-bootstrap estimator for the bias of block bootstrap quantities is inconsistent for almost any sensible choice of the blocking parameters. Some alternative bias estimators are proposed and shown to be consistent.

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