We consider the following version of the standard problem of empirical estimates in stochastic optimization. We assume that the underlying random vectors are independent and not necessarily identically distributed but that they satisfy a "slow variation" condition in the sense of the definition given in this paper. We show that these assumptions along with the usual restrictions (boundedness and equicontinuity) on a class of functions allow one to use the empirical mean method to obtain a consistent sequence of estimates of infimums of the functional to be minimized. Also, we provide certain estimates of the rate of convergence.
@article{bwmeta1.element.bwnjournal-article-doi-10_4064-am41-1-1,
author = {E. Gordienko and J. Ruiz de Ch\'avez and E. Zaitseva},
title = {On convergence of the empirical mean method for non-identically distributed random vectors},
journal = {Applicationes Mathematicae},
volume = {41},
year = {2014},
pages = {1-12},
zbl = {1295.90028},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-doi-10_4064-am41-1-1}
}
E. Gordienko; J. Ruiz de Chávez; E. Zaitseva. On convergence of the empirical mean method for non-identically distributed random vectors. Applicationes Mathematicae, Tome 41 (2014) pp. 1-12. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-doi-10_4064-am41-1-1/