Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership

Zens, Gregor (2019) Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership. Advances in Data Analysis and Classification, 13. pp. 1019-1051. ISSN 1862-5347

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Abstract

A method for implicit variable selection in mixture-of-experts frameworks is proposed. We introduce a prior structure where information is taken from a set of independent covariates. Robust class membership predictors are identified using a normal gamma prior. The resulting model setup is used in a finite mixture of Bernoulli distributions to find homogenous clusters of women in Mozambique based on their information sources on HIV. Fully Bayesian inference is carried out via the implementation of a Gibbs sampler.

Item Type: Article
Additional Information: Open access funding provided by Vienna University of Economics and Business (WU)
Keywords: Mixture-of-experts, Classification, Shrinkage, Bayesian inference, Normal gamma prior
Classification Codes: Mathematics Subject Classification 62F15, 62J07, 62H30, 90-08
Divisions: Departments > Volkswirtschaft
Version of the Document: Published
Depositing User: ePub Administrator
Date Deposited: 22 Feb 2019 12:58
Last Modified: 24 Jan 2020 09:01
Related URLs:
URI: https://epub.wu.ac.at/id/eprint/6843

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