On deconvolution of distribution functions
Abstract
The subject of this paper is the problem of nonparametric estimation of a continuous distribution function from observations with measurement errors. We study minimax complexity of this problem when unknown distribution has a density belonging to the Sobolev class, and the error density is ordinary smooth. We develop rate optimal estimators based on direct inversion of empirical characteristic function. We also derive minimax affine estimators of the distribution function which are given by an explicit convex optimization problem. Adaptive versions of these estimators are proposed, and some numerical results demonstrating good practical behavior of the developed procedures are presented.
Cite
@article{arxiv.1006.3918,
title = {On deconvolution of distribution functions},
author = {I. Dattner and A. Goldenshluger and A. Juditsky},
journal= {arXiv preprint arXiv:1006.3918},
year = {2012}
}
Comments
Published in at http://dx.doi.org/10.1214/11-AOS907 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)