Bayesian Variable Selections for Probit Models with Componentwise Gibbs Samplers

Chang Sheng-Mao; Chen Ray-Bing; Chi Yunchan

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Bayesian Variable Selections for Probit Models with Componentwise Gibbs Samplers

机译：具有基于分量的吉布斯采样器的Probit模型的贝叶斯变量选择

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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.

机译：本文考虑了通过随机搜索变量选择和贝叶斯套索的二元响应的贝叶斯变量选择问题。为了避免在相应的马尔可夫链蒙特卡洛实现中进行矩阵求逆，采用了基于分量的吉布斯采样器（CGS）思想。此外，我们还为提出的方法提出了自动超参数调整规则。仿真研究和一个真实的例子被用来证明所提出的方法的性能。这些结果表明，CGS方法不仅在变量选择方面表现出色，而且批次平均标准误值也比原始方法低，尤其是对于大量协变量而言。

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来源
《Communications in Statistics》 |2016年第8期|2752-2766|共15页
作者
Chang Sheng-Mao; Chen Ray-Bing; Chi Yunchan;
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作者单位

Natl Cheng Kung Univ, Dept Stat, 1 Ta Hsueh Rd, Tainan 70101, Taiwan;

Natl Cheng Kung Univ, Dept Stat, 1 Ta Hsueh Rd, Tainan 70101, Taiwan;

Natl Cheng Kung Univ, Dept Stat, 1 Ta Hsueh Rd, Tainan 70101, Taiwan;

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收录信息美国《科学引文索引》(SCI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Batch mean standard error; Bayesian Lasso; Probit model; Stochastic search variable selection;

机译：批次均值标准误;贝叶斯套索;概率模型;随机搜索变量选择;

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2. Convergence properties of Gibbs samplers for Bayesian probit regression with proper priors [J] . Saptarshi Chakraborty, Kshitij Khare Electronic Journal of Statistics . 2017,第1期

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7. Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler [O] . White, Arthur, Wyse, Jason, Murphy, Thomas Brendan 2015

机译：使用折叠的潜类分析的贝叶斯变量选择吉布斯采样器

Bayesian Variable Selections for Probit Models with Componentwise Gibbs Samplers

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