Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces


The class of location-scale finite mixtures is of enduring interest both from applied and theoretical perspectives of probability and statistics. We 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 $p \in [1,\infty)$, location-scale mixtures of an essentially bounded PDF can approximate any PDF in $L_p$, in the $L_p$ norm.

Communications in Statistics - Theory and Methods
TrungTin Nguyen
TrungTin Nguyen
Postdoctoral Research Fellow

A central theme of my research is data science at the intersection of statistical learning, machine learning and optimization.