Constrained monotone EM algorithms for mixtures of multivariate <Emphasis Type="Italic">t</Emphasis> distributions |
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Authors: | F Greselin S Ingrassia |
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Institution: | (1) Department of Applied-Mathematics, National Chung Hsing University, Taichung, Taiwan;(2) Graduate Institute of Finance, National Chiao Tung University, Hsinchu, Taiwan;(3) Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan |
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Abstract: | Mixtures of multivariate t distributions provide a robust parametric extension to the fitting of data with respect to normal mixtures. In presence of
some noise component, potential outliers or data with longer-than-normal tails, one way to broaden the model can be provided
by considering t distributions. In this framework, the degrees of freedom can act as a robustness parameter, tuning the heaviness of the tails,
and downweighting the effect of the outliers on the parameters estimation. The aim of this paper is to extend to mixtures
of multivariate elliptical distributions some theoretical results about the likelihood maximization on constrained parameter
spaces. Further, a constrained monotone algorithm implementing maximum likelihood mixture decomposition of multivariate t distributions is proposed, to achieve improved convergence capabilities and robustness. Monte Carlo numerical simulations
and a real data study illustrate the better performance of the algorithm, comparing it to earlier proposals. |
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Keywords: | |
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