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

Author： Liu Wei   Zhang Bo   Zhang Zhiwei   Tao Jian   Branscum Adam J.  

Publisher： Taylor & Francis Ltd

E-ISSN： 1563-5163|85|12|2456-2478

ISSN： 0094-9655

Source： Journal of Statistical Computation and Simulation, Vol.85, Iss.12, 2015-08, pp. : 2456-2478

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

Abstract

Related content

Bayesian model selection and parameter estimation for possibly asymmetric and non-stationary time series using a reversible jump Markov chain Monte Carlo approach

By Oh M-S.   Shin D.W.  

Journal of Applied Statistics, Vol. 29, Iss. 5, 2002-07 ,pp. : 771-789

Taylor & Francis Ltd

Access to resources Recommend Favorite

Markov chain Monte Carlo estimation of a mixture item response theory model

By Cho Sun-Joo  

Journal of Statistical Computation and Simulation, Vol. 83, Iss. 2, 2013-02 ,pp. : 278-306

Taylor & Francis Ltd

Access to resources Recommend Favorite

Comparing the performance of a reversible jump Markov chain Monte Carlo algorithm for DNA sequences alignment

By Álvarez Luis  

Journal of Statistical Computation and Simulation, Vol. 76, Iss. 7, 2006-07 ,pp. : 567-584

Taylor & Francis Ltd

Access to resources Recommend Favorite

Bayesian model selection in linear mixed effects models with autoregressive(p) errors using mixture priors

By Fan Tsai-Hung   Wang Yi-Fu   Zhang Yi-Chen  

Journal of Applied Statistics, Vol. 41, Iss. 8, 2014-08 ,pp. : 1814-1829

Taylor & Francis Ltd

Access to resources Recommend Favorite

Bayesian Estimation for the Exponentiated Weibull Model via Markov Chain Monte Carlo Simulation

By Jaheen Zeinhum  

Communications in Statistics: Simulation and Computation, Vol. 40, Iss. 4, 2011-04 ,pp. : 532-543

Taylor & Francis Ltd

Access to resources Recommend Favorite