Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/27164

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
URIhttp://hdl.handle.net/1822/27164
Peer-Reviewedyes
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

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