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

Heinzl, Felix und Tutz, Gerhard (17. November 2011): Clustering in linear mixed models with Dirichlet process mixtures using EM algorithm. Department of Statistics: Technical Reports, Nr. 115

Abstract

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.

• Dokumententyp: Paper
• Publikationsform: Submitted Version
• Keywords: Dirichlet process mixture, mixed models, likelihood inference, EM algorithm
• Fakultät: Mathematik, Informatik und Statistik > Statistik > Technische Reports
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• Sprache: Englisch