Sparse Bayesian variable selection in kernel probit model for analyzing high-dimensional data

被引：0
|
作者：
Aijun Yang
Yuzhu Tian
Yunxian Li
Jinguan Lin
机构：
[1] Nanjing Forestry University,College of Economics and Management
[2] Henan University of Science and Technology,School of Mathematics and Statistics
[3] Yunnan University of Finance and Economics,School of Finance
[4] Nanjing Audit University,School of Statistics and Mathematics
来源：
Computational Statistics | 2020年 / 35卷
关键词：
Variable selection; Correlation prior; Sparse prior; Kernel probit model; Classification;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we developed a sparse Bayesian variable selection in kernel probit model for high-dimensional data classification. Particularly we assigned a correlation prior distribution on the model size and a sparse prior distribution on the regression parameters. MCMC-based computation algorithms are outlined to generate samples from the posterior distributions. Simulation and real data studies show that in terms of the accuracy of variable selection and classification, our proposed method performs better than the other five Bayesian methods without the correlation term in the prior or those involving only one shrinkage parameter.
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页码：245 / 258
页数：13
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