NDLI: Bayesian analysis of finite mixture models of distributions from exponential families

Content Provider	Springer Nature Link
Author	Rufo, M. J. Martín, J. Pérez, C. J.
Copyright Year	2006
Abstract	This paper deals with the Bayesian analysis of finite mixture models with a fixed number of component distributions from natural exponential families with quadratic variance function (NEF-QVF). A unified Bayesian framework addressing the two main difficulties in this context is presented, i.e., the prior distribution choice and the parameter unidentifiability problem. In order to deal with the first issue, conjugate prior distributions are used. An algorithm to calculate the parameters in the prior distribution to obtain the least informative one into the class of conjugate distributions is developed. Regarding the second issue, a general algorithm to solve the label-switching problem is presented. These techniques are easily applied in practice as it is shown with an illustrative example.
Starting Page	621
Ending Page	637
Page Count	17
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	21
Issue Number	3-4
e-ISSN	16139658
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-11-04
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian methods Gibbs sampling Label switching Mixture models Natural exponential family Economic Theory Probability Theory and Stochastic Processes Probability and Statistics in Computer Science Statistics
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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