On the Kernel selection for Minimum-Entropy estimation

Abstract

The purpose of this paper is to investigate the selection of an appropiate kernel to be used in a recent robust approach called mínimum entropy estimator (MEE), This MEE estimator is extended to measurement estimaiion and pdf approximation when p(e) is unknown. The entropy criterion is constructed on the basis of a symmetrized kernel estimate p_n,h (e) of p(e). The MEE performance is generally better than the Maximum Likelihood (ML) estimator. The bandwidth selectian procedure is a crucial task to assure consistency of kernel estimates. Moreover, recent proposed Hilbert kernels avoid the use of bandwidth, improving the consistency of the kernel estimate. A comparison between resuUs obtoined with normal, cosine and Hilbert kernelr is presented.