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.
Dokumententyp: | Zeitschriftenartikel |
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Keywords: | constrained clustering, finite mixture models, label switching, multimodality |
Fakultät: | Mathematik, Informatik und Statistik > Statistik > Technische Reports |
URN: | urn:nbn:de:bvb:19-epub-6336-7 |
Sprache: | Englisch |
Dokumenten ID: | 6336 |
Datum der Veröffentlichung auf Open Access LMU: | 29. Sep. 2008, 13:26 |
Letzte Änderungen: | 04. Nov. 2020, 12:49 |