Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 2752-2766 |
| Number of pages | 15 |
| Journal | Communications in Statistics: Simulation and Computation |
| Volume | 45 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2016 Sep 13 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modelling and Simulation
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Bayesian Variable Selections for Probit Models with Componentwise Gibbs Samplers. / Chang, Sheng-Mao; Chen, Ray-Bing; Chi, Yun-Chan.
In: Communications in Statistics: Simulation and Computation, Vol. 45, No. 8, 13.09.2016, p. 2752-2766.Research output: Contribution to journal › Article
TY - JOUR
T1 - Bayesian Variable Selections for Probit Models with Componentwise Gibbs Samplers
AU - Chang, Sheng-Mao
AU - Chen, Ray-Bing
AU - Chi, Yun-Chan
PY - 2016/9/13
Y1 - 2016/9/13
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84976532962&partnerID=8YFLogxK
U2 - 10.1080/03610918.2014.922983
DO - 10.1080/03610918.2014.922983
M3 - Article
VL - 45
SP - 2752
EP - 2766
JO - Communications in Statistics Part B: Simulation and Computation
JF - Communications in Statistics Part B: Simulation and Computation
SN - 0361-0918
IS - 8
ER -