Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces
TrungTin Nguyen, Faïcel Chamroukhi, Hien D. Nguyen, Geoffrey John McLachlan · Communication in Statistics- Theory and Methods · 2022
The class of location-scale finite mixtures is of enduring interest both from applied and theoretical perspectives of probability and statistics. We establish and prove the following results: to an arbitrary degree of accuracy, (a) location-scale mixtures of a continuous probability density function (PDF) can approximate any continuous PDF, uniformly, on a compact set; and (b) for any finite p≥1, location-scale mixtures of an essentially bounded PDF can approximate any PDF in Lp, in the Lp norm.