RT info:eu-repo/semantics/preprint
T1 Avoiding Spurious Local Maximizers in Mixture Modeling
A1 García Escudero, Luis Ángel
A1 Gordaliza Ramos, Alfonso
A1 Matrán Bea, Carlos
A1 Mayo Iscar, Agustín
A2 Universidad de Valladolid
K1 Statistics
AB The maximum likelihood estimation in the finite mixture of distributions setting isan 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, aconstrained mixture fitting approach is presented with the aim of overcoming the troublesintroduced by spurious solutions. Sound mathematical support is provided and,which is more relevant in practice, a feasible algorithm is also given. This algorithmallows 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 dataanalysts.
YR 2014
FD 2014
LK http://uvadoc.uva.es/handle/10324/5966
UL http://uvadoc.uva.es/handle/10324/5966
LA spa
NO Producción Científica
NO Estadística e IO
DS UVaDOC
RD 23-nov-2024