Abstract
In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.
| Original language | English |
|---|---|
| Pages (from-to) | 2234-2247 |
| Number of pages | 14 |
| Journal | Communications in Statistics: Simulation and Computation |
| Volume | 47 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2018 Sep 14 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modelling and Simulation
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Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models. / Pan, Jia Chiun; Lee, Mei Hsien; Tsai, Miao-Yu.
In: Communications in Statistics: Simulation and Computation, Vol. 47, No. 8, 14.09.2018, p. 2234-2247.Research output: Contribution to journal › Article
TY - JOUR
T1 - Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models
AU - Pan, Jia Chiun
AU - Lee, Mei Hsien
AU - Tsai, Miao-Yu
PY - 2018/9/14
Y1 - 2018/9/14
N2 - In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.
AB - In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.
UR - http://www.scopus.com/inward/record.url?scp=85025833700&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85025833700&partnerID=8YFLogxK
U2 - 10.1080/03610918.2017.1341525
DO - 10.1080/03610918.2017.1341525
M3 - Article
AN - SCOPUS:85025833700
VL - 47
SP - 2234
EP - 2247
JO - Communications in Statistics Part B: Simulation and Computation
JF - Communications in Statistics Part B: Simulation and Computation
SN - 0361-0918
IS - 8
ER -