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

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

doi:10.1080/00949655.2014.931584

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

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

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	2456-2478
Number of pages	23
Journal	Journal of Statistical Computation and Simulation
Volume	85
Issue number	12
DOIs	https://doi.org/10.1080/00949655.2014.931584
State	Published - Aug 13 2015
Externally published	Yes

ASJC Scopus Subject Areas

Statistics and Probability
Modeling and Simulation
Statistics, Probability and Uncertainty
Applied Mathematics

Keywords

Bayesian variable selection
Metropolis–Hastings algorithm
finite normal mixtures
reversible jump algorithm
weighted prior

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10.1080/00949655.2014.931584

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Model selection in finite mixture of regression models: a Bayesian approach with innovative weighted g priors and reversible jump Markov chain Monte Carlo implementation. / Liu, Wei; Zhang, Bo; Zhang, Zhiwei et al.
In: Journal of Statistical Computation and Simulation, Vol. 85, No. 12, 13.08.2015, p. 2456-2478.

Research output: Contribution to journal › Article › peer-review

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author = "Wei Liu and Bo Zhang and Zhiwei Zhang and Jian Tao and Branscum, {Adam J.}",

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year = "2015",

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AU - Branscum, Adam J.

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

ASJC Scopus Subject Areas

Keywords

Access to Document

OpenUrl availability

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