2026-04-14T21:05:22Zhttps://uvadoc.uva.es/oai/request

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

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