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Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership. (English) Zbl 1474.62088

Summary: 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.

MSC:

62F15 Bayesian inference
62J07 Ridge regression; shrinkage estimators (Lasso)
62H30 Classification and discrimination; cluster analysis (statistical aspects)
90-08 Computational methods for problems pertaining to operations research and mathematical programming
62P10 Applications of statistics to biology and medical sciences; meta analysis
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