Testing variance components in nonlinear mixed effects models

Date(s) : 09/10/2017   iCal
14 h 00 min - 15 h 00 min

Joint work with Charlotte Baey and Paul-Henry Cournède (CentraleSupélec, MICS)

Mixed effects models are widely used to describe inter and intra individual variabilities in a population. A fundamental question when adjusting such a model to the population consists in identifying the parameters carrying the different types of variabilities, i.e. those that can be considered constant in the population, referred to as fixed effects, and those that vary among individuals, referred to as random effects.

In this work, we propose a test procedure based on the likelihood ratio one for testing if the variances of a subset of the random effects are equal to zero. The standard theoretical results on the asymptotic distribution of the likelihood ratio test can not be applied in our context. Indeed the assumptions required are not fulfilled since the tested parameter values are on the boundary of the parameter space. The issue of variance components testing has been addressed in the context of linear mixed effects models by several authors and in the particular case of testing the variance of one single random effect in nonlinear mixed effects models. We address 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 distributions, and identify the weights of the mixture. We highlight that the limit distribution depends on the presence or not of correlations between the random effects. We present numerical tools to compute the corresponding quantiles. Finally, we illustrate the finite sample size properties of the test procedure through simulation studies and on real data.


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