Bayesian Variable Selections for Probit Models with Componentwise Gibbs Samplers

Sheng Mao Chang, Ray Bing Chen, Yunchan Chi

研究成果: Article同行評審

5 引文 斯高帕斯(Scopus)


This article considers Bayesian variable selection problems for binary responses via stochastic search variable selection and Bayesian Lasso. To avoid matrix inversion in the corresponding Markov chain Monte Carlo implementations, the componentwise Gibbs sampler (CGS) idea is adopted. Moreover, we also propose automatic hyperparameter tuning rules for the proposed approaches. Simulation studies and a real example are used to demonstrate the performances of the proposed approaches. These results show that CGS approaches do not only have good performances in variable selection but also have the lower batch mean standard error values than those of original methods, especially for large number of covariates.

頁(從 - 到)2752-2766
期刊Communications in Statistics: Simulation and Computation
出版狀態Published - 2016 9月 13

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 建模與模擬


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