Local mixture models of exponential families

dc.creatorAnaya-Izquierdo, Karim
dc.creatorMarriott, Paul
dc.date2007-09-04
dc.date.accessioned2026-07-07T08:28:58Z
dc.date.available2026-07-07T08:28:58Z
dc.descriptionExponential families are the workhorses of parametric modelling theory. One reason for their popularity is their associated inference theory, which is very clean, both from a theoretical and a computational point of view. One way in which this set of tools can be enriched in a natural and interpretable way is through mixing. This paper develops and applies the idea of local mixture modelling to exponential families. It shows that the highly interpretable and flexible models which result have enough structure to retain the attractive inferential properties of exponential families. In particular, results on identification, parameter orthogonality and log-concavity of the likelihood are proved.
dc.descriptionPublished at http://dx.doi.org/10.3150/07-BEJ6170 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)
dc.identifierhttps://arxiv.org/abs/0709.0447
dc.identifierhttp://arxiv.org/abs/0709.0447
dc.identifierBernoulli 2007, Vol. 13, No. 3, 623-640
dc.identifierdoi:10.3150/07-BEJ6170
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/137805
dc.subjectStatistics Theory
dc.titleLocal mixture models of exponential families
dc.typetext

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