Sparse Bayesian variable selection in kernel probit model for analyzing high-dimensional data
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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.
KeywordsVariable selection Correlation prior Sparse prior Kernel probit model Classification
The authors gratefully acknowledge the financial support of the Humanities and Social Science Foundation of Ministry of Education of China (18YJC910001), the Natural Science Foundation of China (11501294,11501167,11571073), the University Philosophy and Social Science Research Project of Jiangsu Province (2018SJA0130) and the Jiangsu Qinglan Project(2017).
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Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest
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