2024-03-29T09:03:03Zhttp://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/59662021-06-23T10:11:08Zcom_10324_1151com_10324_931com_10324_894col_10324_1936
00925njm 22002777a 4500
dc
García Escudero, Luis Ángel
author
Gordaliza Ramos, Alfonso
author
Matrán Bea, Carlos
author
Mayo Iscar, Agustín
author
2014
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.
http://uvadoc.uva.es/handle/10324/5966
Statistics
Avoiding Spurious Local Maximizers in Mixture Modeling