Please use this identifier to cite or link to this item:

TitleMeasuring the component overlapping in mixtures of linear regressions
Author(s)Faria, Susana
Soromenho, Gilda
KeywordsMixtures of linear regressions
Entropy criterion
Kullback-Leiber information
Simulation study
Issue dateJul-2013
PublisherStatistical Modelling Society
Abstract(s)Entropy-type measures for the heterogeneity of data have been used for a long time. In a mixture model context, entropy criterions can be used to measure the overlapping of the mixture components. In this paper we study an entropy-based criterion in mixtures of linear regressions to measure the closeness between the mixture components. We show how an entropy criterion can be derived based on the Kullback-Leiber distance, which is a measure of distance between probability distributions. To investigate the e ectiveness of the proposed criterion, a simulation study was performed.
TypeConference paper
AccessOpen access
Appears in Collections:CMAT - Artigos em atas de conferências e capítulos de livros com arbitragem / Papers in proceedings of conferences and book chapters with peer review

Files in This Item:
File Description SizeFormat 
sfariagsoromenhoIWSM28.pdfDocumento Principal75,13 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID