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On a loss-based prior for the number of components in mixture models

Grazian, Clara, Villa, Cristiano, Liseo, Brunero (2020) On a loss-based prior for the number of components in mixture models. Statistics and Probability Letters, 158 . p. 108656. ISSN 0167-7152. (doi:10.1016/j.spl.2019.108656) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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https://doi.org/10.1016/j.spl.2019.108656

Abstract

We introduce a prior distribution for the number of components of a mixture model. The prior considers the worth of each possible mixture, measured by a loss function with two components: one measures the loss in information in choosing the wrong mixture and one the loss due to complexity.

Item Type: Article
DOI/Identification number: 10.1016/j.spl.2019.108656
Uncontrolled keywords: Mixture models, Bayesian inference, Default priors, Loss-based priors, Clustering
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Cristiano Villa
Date Deposited: 26 Oct 2019 13:24 UTC
Last Modified: 03 Dec 2019 13:51 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/77890 (The current URI for this page, for reference purposes)
Villa, Cristiano: https://orcid.org/0000-0002-2670-2954
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