Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership
Econometrics
2019-01-15 v2
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.
Keywords
Cite
@article{arxiv.1809.04853,
title = {Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership},
author = {Gregor Zens},
journal= {arXiv preprint arXiv:1809.04853},
year = {2019}
}