Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jeconom.2012.06.012
DC FieldValue
dc.titleGeneralized smooth finite mixtures
dc.contributor.authorVillani, M.
dc.contributor.authorKohn, R.
dc.contributor.authorNott, D.J.
dc.date.accessioned2014-10-28T05:16:36Z
dc.date.available2014-10-28T05:16:36Z
dc.date.issued2012-12
dc.identifier.citationVillani, 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
dc.identifier.issn03044076
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105471
dc.description.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. © 2012 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.jeconom.2012.06.012
dc.sourceScopus
dc.subjectBayesian inference
dc.subjectConditional distribution
dc.subjectGLM
dc.subjectMarkov chain Monte Carlo
dc.subjectMixture of experts
dc.subjectVariable selection
dc.typeConference Paper
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1016/j.jeconom.2012.06.012
dc.description.sourcetitleJournal of Econometrics
dc.description.volume171
dc.description.issue2
dc.description.page121-133
dc.description.codenJECMB
dc.identifier.isiut000311470500003
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