We show that the empirical process of the block-based bootstrap observations from a stationary sequence converges weakly to an appropriate Gaussian process, conditionally in probability and almost surely, depending upon the block length. This bootstrap was introduced by Kunsch and later by Liu and Singh. Applications in estimation of the sampling distribution of a compactly differentiable functional are indicated.
Publié le : 1994-06-14
Classification:
Bootstrap,
empirical processes,
weak convergence,
stationary and mixing sequences,
compactly differentiable functionals,
62G08,
62M99
@article{1176325507,
author = {Naik-Nimbalkar, U. V. and Rajarshi, M. B.},
title = {Validity of Blockwise Bootstrap for Empirical Processes with Stationary Observations},
journal = {Ann. Statist.},
volume = {22},
number = {1},
year = {1994},
pages = { 980-994},
language = {en},
url = {http://dml.mathdoc.fr/item/1176325507}
}
Naik-Nimbalkar, U. V.; Rajarshi, M. B. Validity of Blockwise Bootstrap for Empirical Processes with Stationary Observations. Ann. Statist., Tome 22 (1994) no. 1, pp. 980-994. http://gdmltest.u-ga.fr/item/1176325507/