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Title: Avoiding Spurious Local Maximizers in Mixture Modeling
Authors: García Escudero, Luis Angel
Gordaliza Ramos, Alfonso
Matrán Bea, Carlos
Mayo Iscar, Agustín
Editors: Universidad de Valladolid
Issue Date: 2014
Description: Producción Científica
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.
Keywords: Statistics
Departament : Estadística e IO
Language: spa
Rights: info:eu-repo/semantics/openAccess
Appears in Collections:DEP24 - Otros Documentos (Monografías, Informes, Memorias, Documentos de Trabajo, etc)

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