TY  - JOUR
TI  - Dealing with label switching in mixture models under genuine multimodality
AB  - The fitting of finite mixture models is an ill-defined estimation problem as completely
different parameterizations can induce similar mixture distributions. This
leads to multiple modes in the likelihood which is a problem for frequentist maximum
likelihood estimation, and complicates statistical inference of Markov chain
Monte Carlo draws in Bayesian estimation. For the analysis of the posterior density
of these draws a suitable separation into different modes is desirable. In addition, a
unique labelling of the component specific estimates is necessary to solve the label
switching problem. This paper presents and compares two approaches to achieve
these goals: relabelling under multimodality and constrained clustering. The algorithmic
details are discussed and their application is demonstrated on artificial and
real-world data.
SP  - 851
EP  - 861
UR  - http://epub.ub.uni-muenchen.de/6336/1/tr039.pdf
PY  - 2009-05-01
JO  - Journal of Multivariate Analysis
AU  - Grün, Bettina 
AU  - Leisch, Friedrich 
ER -