Weighted uniform consistency of kernel density estimators

Abstract

Let f_n denote a kernel density estimator of a continuous density f in d dimensions, bounded and positive. Let Ψ(t) be a positive continuous function such that \|Ψf^β\|_{\infty}<\infty for some 0<β<1/2. Under natural smoothness conditions, necessary and sufficient conditions for the sequence \sqrt\frac{nh_n^d}{2|\log h_n^d|}\|Ψ(t)(f_n(t)-Ef_n(t))\|_{\infty} to be stochastically bounded and to converge a.s. to a constant are obtained. Also, the case of larger values of βis studied where a similar sequence with a different norming converges a.s. either to 0 or to +\infty, depending on convergence or divergence of a certain integral involving the tail probabilities of Ψ(X). The results apply as well to some discontinuous not strictly positive densities.

Published by the Institute of Mathematical Statistics (http://www.imstat.org) in the Annals of Probability (http://www.imstat.org/aop/) at http://dx.doi.org/10.1214/009117904000000063

References in corpus (1)

• A uniform functional law of the logarithm for the local empirical process

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