Generalized Smooth Finite Mixtures
2012 (English)In: Journal of Econometrics, ISSN 0304-4076, E-ISSN 1872-6895, Vol. 171, no 2, 121-133 p.Article in journal (Refereed) Published
We 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 overtting. We use simulated and real datasets to illustrate the methodology
Place, publisher, year, edition, pages
Elsevier, 2012. Vol. 171, no 2, 121-133 p.
Bayesian inference, Conditional distribution, GLM, Markov Chain Monte Carlo, Mixture of Experts, Variable selection.
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-79582DOI: 10.1016/j.jeconom.2012.06.012ISI: 000311470500003OAI: oai:DiVA.org:liu-79582DiVA: diva2:543808