Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jeconom.2012.06.012
Title: Generalized smooth finite mixtures
Authors: Villani, M.
Kohn, R.
Nott, D.J. 
Keywords: Bayesian inference
Conditional distribution
GLM
Markov chain Monte Carlo
Mixture of experts
Variable selection
Issue Date: Dec-2012
Citation: Villani, M., Kohn, R., Nott, D.J. (2012-12). Generalized smooth finite mixtures. Journal of Econometrics 171 (2) : 121-133. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jeconom.2012.06.012
Abstract: 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 overfitting. We use simulated and real data sets to illustrate the methodology. © 2012 Elsevier B.V. All rights reserved.
Source Title: Journal of Econometrics
URI: http://scholarbank.nus.edu.sg/handle/10635/105471
ISSN: 03044076
DOI: 10.1016/j.jeconom.2012.06.012
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