Clustering in linear mixed models with approximate Dirichlet process mixtures using EM algorithm

In linear mixed models, the assumption of normally distributed
random effects is often inappropriate and unnecessarily
restrictive. The proposed approximate Dirichlet process mixture
assumes a hierarchical Gaussian mixture that is based on the
truncated version of the stick breaking presentation of the
Dirichlet process. In addition to the weakening of distributional
assumptions, the specification allows to identify clusters of
observations with a similar random effects structure. An
Expectation-Maximization algorithm is given that solves the
estimation problem and that, in certain respects, may exhibit
advantages over Markov chain Monte Carlo approaches when modelling
with Dirichlet processes. The method is evaluated in a simulation
study and applied to the dynamics of unemployment in Germany as
well as lung function growth data.