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

By Felix Heinzl and Gerhard Tutz


In linear mixed models the assumption of normally distributed random effects is often inappropriate and unnecessary restrictive. The proposed Dirichlet process mixture assumes a hierarchical Gaussian mixture. In addition to the weakening of distributions assumptions the specification allows to estimate clusters of observations with a similar random effects structure identified. An Expectation-Maximization algorithm is given that solves the estimation problem and that exhibits advantages over in this framework usually used Markov chain Monte Carlo approaches. The method is evaluated in a simulation study and applied to dynamics of unemployment in Germany as well as lung function growth data.

Topics: Technische Reports, ddc:510
Year: 2011
DOI identifier: 10.1177/1471082x12471372
OAI identifier:
Provided by: Open Access LMU

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