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.
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:6843 |
| Date | 13 February 2019 |
| Creators | Zens, Gregor |
| Publisher | Springer |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf |
| Rights | Creative Commons: Attribution 4.0 International (CC BY 4.0) |
| Relation | http://dx.doi.org/10.1007/s11634-019-00353-y, https://www.springer.com/de, http://epub.wu.ac.at/6843/ |
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