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UID:1628@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20170313T140000
DTEND;TZID=Europe/Paris:20170313T150000
DTSTAMP:20170226T130000Z
URL:https://www.i2m.univ-amu.fr/evenements/a-semiparametric-and-location-s
 hift-copula-based-mixture-model/
SUMMARY: (...): A semiparametric and location-shift copula-based mixture mo
 del
DESCRIPTION:: Modeling of distributions mixtures has rested on Gaussian dis
 tributions and/or a conditional independence hypothesis for a long time. O
 nly recently have researchers begun to construct and study broader generic
  models without appealing to such hypotheses. Some of these extensions use
  copulas as a tool to build flexible models\, as they permit to model the 
 dependence and the marginal distributions separately. But this approach al
 so has drawbacks. First\, the practitioner has to make more arbitrary choi
 ces\, and second\, marginal misspecification may loom on the horizon. This
  paper aims at overcoming these limitations by presenting a copula-based m
 ixture model which is semiparametric. Thanks to a location-shift hypothesi
 s\, semiparametric estimation\, also\, is feasible\, allowing for data ada
 ptation without any modeling effort.http://perso.uclouvain.be/gildas.mazo/
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DTSTART:20161030T020000
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