Dealing with label switching in mixture models under genuine multimodality

JOURNAL OF MULTIVARIATE ANALYSIS

卷:100

Article

Gruen, Bettina¹  Leisch, Friedrich² 

[1] Vienna Univ Econ & Business Adm, Dept Math & Stat, A-1090 Vienna, Austria

[2] Univ Munich, Inst Stat, D-80539 Munich, Germany

关键词: Constrained clustering;    Finite mixture models;    Label switching;    Multimodality;   

DOI  :  10.1016/j.jmva.2008.09.006

来源: Elsevier

【 摘 要 】

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 or 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. (C) 2008 Elsevier Inc. All rights reserved.

【 授权许可】

Free