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UID:1926@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20171009T140000
DTEND;TZID=Europe/Paris:20171009T150000
DTSTAMP:20170924T120000Z
URL:https://www.i2m.univ-amu.fr/evenements/testing-variance-components-in-
 nonlinear-mixed-effects-models/
SUMMARY: (...): Testing variance components in nonlinear mixed effects mode
 ls
DESCRIPTION:: Joint work with Charlotte Baey and Paul-Henry Cournède (Cent
 raleSupélec\, MICS)Mixed effects models are widely used to describe inter
  and intra individual variabilities in a population. A fundamental questio
 n when adjusting such a model to the population consists in identifying th
 e parameters carrying the different types of variabilities\, i.e. those th
 at can be considered constant in the population\, referred to as fixed eff
 ects\, and those that vary among individuals\, referred to as random effec
 ts.In this work\, we propose a test procedure based on the likelihood rati
 o one for testing if the variances of a subset of the random effects are e
 qual to zero. The standard theoretical results on the asymptotic distribut
 ion of the likelihood ratio test can not be applied in our context. Indeed
  the assumptions required are not fulfilled since the tested parameter val
 ues are on the boundary of the parameter space. The issue of variance comp
 onents testing has been addressed in the context of linear mixed effects m
 odels by several authors and in the particular case of testing the varianc
 e of one single random effect in nonlinear mixed effects models. We addres
 s the case of testing that the variances of a subset of the random effects
  are equal to zero. We proof that the asymptotic distribution of the test 
 is a chi bar square distribution\, indeed a mixture of chi square distribu
 tions\, and identify the weights of the mixture. We highlight that the lim
 it distribution depends on the presence or not of correlations between the
  random effects. We present numerical tools to compute the corresponding q
 uantiles. Finally\, we illustrate the finite sample size properties of the
  test procedure through simulation studies and on real data.http://www.res
 earchgate.net/profile/Estelle_Kuhn
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