Model selection in finite mixture of regression models: a Bayesian approach with innovative weightedgpriors and reversible jump Markov chain Monte Carlo implementation

DOI10.1080/00949655.2014.931584zbMath1457.62023OpenAlexW2089980140MaRDI QIDQ5220880

Zhiwei Zhang, Wei Liu, Jian Tao, Bo Zhang, Adam J. Branscum

Publication date: 27 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2014.931584

zbMATH Keywords

Metropolis-Hastings algorithmBayesian variable selectionfinite normal mixturesreversible jump algorithmweighted prior

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Linear regression; mixed models (62J05) Bayesian inference (62F15)

Related Items (4)

Variable selection in finite mixture of regression models with an unknown number of components ⋮ Bayesian analysis for heteroscedastic normal-Pareto mixture model with application ⋮ A data-driven reversible jump for estimating a finite mixture of regression models ⋮ Bayesian variable selection in linear regression models with non-normal errors

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