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Estimating Mixtures of Regressions

Merrilee Hurn, Ana Justel and Christian P. Robert
Journal of Computational and Graphical Statistics
Vol. 12, No. 1 (Mar., 2003), pp. 55-79
Stable URL: http://www.jstor.org/stable/1391069
Page Count: 25
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Estimating Mixtures of Regressions
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Abstract

This article shows how Bayesian inference for switching regression models and their generalizations can be achieved by the specification of loss functions which overcome the label switching problem common to all mixture models. We also derive an extension to models where the number of components in the mixture is unknown, based on the birth-and-death technique developed in recent literature. The methods are illustrated on various real datasets.

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