Using gaussian mixtures with unknownnumber of components for mixed

Hierarchical mixed models are used to account for dependence between correlated data, in particular dependence created by a group structure within the sample. In such models, the correlation between observations is modelled by including, in the regression model, group-indexed parameters regarded as random variables, so called random eeects. Gaussian distributions are commonly used for the random eeects. However, this choice places a strong constraint on the shape of the random parameter distribution.In this presentation, we focus on misspeciication in mixed model with random intercept, a commonly used model in epidemiology. We propose to model the prior distribution of the random intercept by gaussian mixtures with an unknown number of components in a Bayesian framework. This methodology has recently been developed by Richard-son & Green (1997) to analyse heterogeneous data. Another use of gaussian mixtures with unknown number of components is that of density estimation.