Grün, Bettina, Leisch, Friedrich. 2009. Dealing with label switching in mixture models under genuine multimodality. Journal of Multivariate Analysis 100 (5): 851-861.

RIS

@ARTICLE{Grün2009,
title = {Dealing with label switching in mixture models under genuine multimodality},
author = {Bettina Grün and Friedrich Leisch},
year = {2009},
url = {http://epub.ub.uni-muenchen.de/6336/1/tr039.pdf},
volume = {100},
number = {5},
language = {EN},
pages = {851-861},
journal = {Journal of Multivariate Analysis},
abstract = {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.},
}