A new Bayesian approach for determining the number of components in a finite mixture

Aitkin, Murray and Vu, Duy and Francis, Brian (2015) A new Bayesian approach for determining the number of components in a finite mixture. Metron, 73 (2). pp. 155-175. ISSN 0026-1424

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Abstract

This article evaluates a new Bayesian approach to determining the number of components in a finite mixture. We evaluate through simulation studies mixtures of normals and latent class mixtures of Bernoulli responses. For normal mixtures we use a “gold standard” set of population models based on a well-known “testbed” data set – the galaxy recession velocity data set of Roeder (1990). For Bernoulli latent class mixtures we consider models for psychiatric diagnosis (Berkhof, van Mechelen and Gelman 2003). The new approach is based on comparing models with different numbers of components through their posterior deviance distributions, based on non-informative or diffuse priors. Simulations show that even large numbers of closely spaced normal components can be identified with sufficiently large samples, while for atent classes with Bernoulli responses identification is more complex, though it again improves with increasing sample size.

Item Type:
Journal Article
Journal or Publication Title:
Metron
Additional Information:
(included in attached document) The final publication is available at Springer via http://dx.doi.org/10.1007/s40300-015-0068-1
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
74432
Deposited By:
Deposited On:
30 Jun 2015 13:22
Refereed?:
Yes
Published?:
Published
Last Modified:
31 Oct 2020 03:30