Simultaneous variable selection and component selection for regression density estimation with mixtures of heteroscedastic experts

DOI10.1214/12-EJS705zbMath1295.62039MaRDI QIDQ1950853

Minh-Ngoc Tran, Robert Kohn, David J. Nott

Publication date: 28 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1340974140

zbMATH Keywords

heteroscedasticityBayesian model selectionvariational approximationmixture of normals

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07)

Related Items (6)

Bayesian variable selection and coefficient estimation in heteroscedastic linear regression model ⋮ Model selection for the localized mixture of experts models ⋮ POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES ⋮ Bayesian variable selection for finite mixture model of linear regressions ⋮ Flexible multivariate regression density estimation ⋮ Model selection in finite mixture of regression models: a Bayesian approach with innovative weightedgpriors and reversible jump Markov chain Monte Carlo implementation

Uses Software

• SemiPar
• PRMLT

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