Explicit rates of approximation in the CLT for quadratic forms
Abstract
Let be i.i.d. -valued real random vectors. Assume that , , and that is not concentrated in a proper subspace of . Let be a mean zero Gaussian random vector with the same covariance operator as that of . We study the distributions of nondegenerate quadratic forms of the normalized sums and show that, without any additional conditions, provided that and the fourth moment of exists. Furthermore, we provide explicit bounds of order for for the rate of approximation by short asymptotic expansions and for the concentration functions of the random variables , . The order of the bound is optimal. It extends previous results of Bentkus and G\"{o}tze [Probab. Theory Related Fields 109 (1997a) 367-416] (for ) to the case , which is the smallest possible dimension for such a bound. Moreover, we show that, in the finite dimensional case and for isometric , the implied constant in has the form with some depending on only. This answers a long standing question about optimal rates in the central limit theorem for quadratic forms starting with a seminal paper by Ess\'{e}en [Acta Math. 77 (1945) 1-125].
Cite
@article{arxiv.1104.0519,
title = {Explicit rates of approximation in the CLT for quadratic forms},
author = {Friedrich Götze and Andrei Yu. Zaitsev},
journal= {arXiv preprint arXiv:1104.0519},
year = {2014}
}
Comments
Published in at http://dx.doi.org/10.1214/13-AOP839 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org)