2026-04-23T21:21:16Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/59662021-06-23T10:11:08Zcom_10324_1151com_10324_931com_10324_894col_10324_1936

Avoiding Spurious Local Maximizers in Mixture Modeling García Escudero, Luis Ángel Gordaliza Ramos, Alfonso Matrán Bea, Carlos Mayo Iscar, Agustín Universidad de Valladolid Statistics Producción Científica 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. 2014-09-15T20:07:44Z 2014-09-15T20:07:44Z 2014 info:eu-repo/semantics/preprint http://uvadoc.uva.es/handle/10324/5966 spa info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Attribution 4.0 International