On inconsistency of the jackknife-after-bootstrap bias estimator for dependent data

  • Soumendra Nath Lahiri

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)15-34
Number of pages20
JournalJournal of Multivariate Analysis
Volume63
Issue number1
DOIs
StatePublished - Oct 1997

Keywords

  • Block bootstrap
  • Consistency
  • Jackknife
  • Weak dependence

Fingerprint

Dive into the research topics of 'On inconsistency of the jackknife-after-bootstrap bias estimator for dependent data'. Together they form a unique fingerprint.

Cite this