Avoiding spurious local maximizers in mixture modeling

Abstract

The maximum likelihood estimation in the finite mixture of distributions setting is an ill-posed problem that is treatable, in practice, through the EM algorithm. However, the existence of spurious solutions (singularities and non-interesting local maximizers) makes difficult to find sensible mixture fits for non-expert practitioners. In this work, a constrained mixture fitting approach is presented with the aim of overcoming the troubles introduced by spurious solutions. Sound mathematical support is provided and, which is more relevant in practice, a feasible algorithm is also given. This algorithm allows for monitoring solutions in terms of the constant involved in the restrictions, which yields a natural way to discard spurious solutions and a valuable tool for data analysts.

DOI: 10.1007/s11222-014-9455-3

Extracted Key Phrases

9 Figures and Tables

Cite this paper

@article{GarcaEscudero2015AvoidingSL, title={Avoiding spurious local maximizers in mixture modeling}, author={Luis Angel Garc{\'i}a-Escudero and Alfonso Gordaliza and Carlos Matr{\'a}n and Agust{\'i}n Mayo-Iscar}, journal={Statistics and Computing}, year={2015}, volume={25}, pages={619-633} }