Preprint #96-1
On the Jackknife-After-Bootstrap Method for Dependent
Data and its Consistency Properties
by
S.N. Lahiri
Abstract
Motivated by Efron (1992), this paper proposes a version of the moving block
jackknife as a method of estimating standard errors of block bootstrap
estimators under dependence. As in the case of iid observations, the
proposed method merely regroups the values of statistics from different
bootstrap replicates to produce an estimate of its standard error.
Consistency of the resulting standard error estimator is proved for block
bootstrap estimators of different population parameters related to the unknown
sampling distributions of a large class of statistics. Consistency of Efron
(1992)'s method is also established in similar problems for iid data.
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