Dealing with label switching in mixture models under genuine multimodality
AbstractThe 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 100 (2009)
Issue (Month): 5 (May)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
Find related papers by JEL classification:
- 62H - - - - - -
- 62F - - - - - -
- Con - Mathematical and Quantitative Methods - - - - -
- clu - - - - - -
- Fin - International Economics - - - - -
- mix - - - - - -
- mod - - - - - -
- Lab - Industrial Organization - - - - -
- swi - - - - - -
- Mul - Business Administration and Business Economics; Marketing; Accounting - - - - -
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