Generalized smooth finite mixtures
AbstractWe propose a general class of models and a unified Bayesian inference methodology for flexibly estimating the density of a response variable conditional on a possibly high-dimensional set of covariates. Our model is a finite mixture of component models with covariate-dependent mixing weights. The component densities can belong to any parametric family, with each model parameter being a deterministic function of covariates through a link function. Our MCMC methodology allows for Bayesian variable selection among the covariates in the mixture components and in the mixing weights. The model’s parameterization and variable selection prior are chosen to prevent overfitting. We use simulated and real data sets to illustrate the methodology.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 171 (2012)
Issue (Month): 2 ()
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Web page: http://www.elsevier.com/locate/jeconom
Bayesian inference; Conditional distribution; GLM; Markov chain Monte Carlo; Mixture of experts; Variable selection;
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