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Por favor, use este identificador para citar o enlazar este ítem: http://uvadoc.uva.es/handle/10324/5966
Título: Avoiding Spurious Local Maximizers in Mixture Modeling
Autor: García Escudero, Luis Angel
Gordaliza Ramos, Alfonso
Matrán Bea, Carlos
Mayo Iscar, Agustín
Editor: Universidad de Valladolid
Año del Documento: 2014
Descripción: Producción Científica
Resumen: 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.
Materias (normalizadas): Statistics
Departamento: Estadística e IO
Idioma: spa
URI: http://uvadoc.uva.es/handle/10324/5966
Derechos: info:eu-repo/semantics/openAccess
Aparece en las colecciones:DEP24 - Otros Documentos (Informes, Memorias, Documentos de Trabajo, etc)

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