Dealing with label switching in mixture models under genuine multimodality

Bettina Grn and Friedrich Leisch

Journal of Multivariate Analysis, 2009, vol. 100, issue 5, 851-861

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.

Keywords: 62H30; 62F15; Constrained; clustering; Finite; mixture; models; Label; switching; Multimodality (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (5)

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