Concentration inequalities for sampling without replacement
Abstract
Concentration inequalities quantify the deviation of a random variable from a fixed value. In spite of numerous applications, such as opinion surveys or ecological counting procedures, few concentration results are known for the setting of sampling without replacement from a finite population. Until now, the best general concentration inequality has been a Hoeffding inequality due to Serfling [Ann. Statist. 2 (1974) 39-48]. In this paper, we first improve on the fundamental result of Serfling [Ann. Statist. 2 (1974) 39-48], and further extend it to obtain a Bernstein concentration bound for sampling without replacement. We then derive an empirical version of our bound that does not require the variance to be known to the user.
Cite
@article{arxiv.1309.4029,
title = {Concentration inequalities for sampling without replacement},
author = {Rémi Bardenet and Odalric-Ambrym Maillard},
journal= {arXiv preprint arXiv:1309.4029},
year = {2015}
}
Comments
Published at http://dx.doi.org/10.3150/14-BEJ605 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)