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Generalized Smooth Finite Mixtures
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, The Institute of Technology.
University of New South Wales, Sydney, Australia.
National University of Singapore.
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
Abstract [en]

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.
Keyword [en]
Bayesian inference, Conditional distribution, GLM, Markov Chain Monte Carlo, Mixture of Experts, Variable selection.
National Category
Probability Theory and Statistics
URN: urn:nbn:se:liu:diva-79582DOI: 10.1016/j.jeconom.2012.06.012ISI: 000311470500003OAI: diva2:543808
Available from: 2012-08-10 Created: 2012-08-10 Last updated: 2013-01-14

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Villani, Mattias
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