Quotation Frühwirth-Schnatter, Sylvia. 2010. Identifying mixture models under model uncertainty. ERCIM’10, 3rd International Conference of the ERCIM WG on Computing and Statistics, Senate House, University of London, Großbritannien, 10.12.-12.12. Invited Talk


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Abstract

The identification of finite mixture models when the number of components is unknown is considered. The first part of the talk sheds some light on the role the prior of the weight distribution p(h) plays when the true number of components is unknown. It is shown that the very popular uniform prior is usually a poor choice for overfitting models. A prior decision has to be made through the choice of p(h) whether for overfitting mixture models empty components or identical, non-empty components should be introduced. As a consequence of this choice, either the number of nonempty components or the total of components is a better estimator of the true number of components. In the second part of the talk identification of finite mixture models that are strongly overfitting heterogeneity in the component-specific parameters is discussed. While standard priors lead to underfitting the true number of components, shrinkage priors well-known from variable selection are applied to handle overfitting heterogeneity. Such priors are able to discriminate between coefficients which are more or less homogenous and coefficients which are heterogeneous and avoid underfitting of the number of components by reducing automatically the prior variance of homogeneous components.

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Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title Identifying mixture models under model uncertainty
Event ERCIM’10, 3rd International Conference of the ERCIM WG on Computing and Statistics
Year 2010
Date 10.12.-12.12
Country United Kingdom
Location Senate House, University of London
URL http://cfe-csda.org/cfe10/LondonBoA.pdf
Invited Talk Y

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Frühwirth-Schnatter, Sylvia (Details)
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