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Jeffreys priors for mixture estimation : properties and alternatives

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Grazian, Clare and Robert, Christian P. (2018) Jeffreys priors for mixture estimation : properties and alternatives. Computational Statistics & Data Analysis, 121 . pp. 149-163. doi:10.1016/j.csda.2017.12.005

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Official URL: https://doi.org/10.1016/j.csda.2017.12.005

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Abstract

While Jeffreys priors usually are well-defined for the parameters of mixtures of distributions, they are not available in closed form. Furthermore, they often are improper priors. Hence, they have never been used to draw inference on the mixture parameters. The implementation and the properties of Jeffreys priors in several mixture settings are studied. It is shown that the associated posterior distributions most often are improper. Nevertheless, the Jeffreys prior for the mixture weights conditionally on the parameters of the mixture components will be shown to have the property of conservativeness with respect to the number of components, in case of overfitted mixture and it can be therefore used as a default priors in this context.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Bayesian statistical decision theory, Mathematical statistics, Neural networks (Computer science)
Journal or Publication Title: Computational Statistics & Data Analysis
Publisher: Elsevier Science Ltd
ISSN: 0167-9473
Official Date: May 2018
Dates:
DateEvent
May 2018Published
2 January 2018Available
19 December 2017Accepted
Volume: 121
Page Range: pp. 149-163
DOI: 10.1016/j.csda.2017.12.005
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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