Sparse Bayesian multinomial probit regression model with correlation prior for high-dimensional data classification

Aijun Yang, Xuejun Jiang, Pengfei Liu and Jinguan Lin

Statistics & Probability Letters, 2016, vol. 119, issue C, 241-247

Abstract: Selecting a small number of relevant genes for cancer classification has received a great deal of attention in microarray data analysis. In this paper, a sparse Bayesian multinomial probit regression model with correlation prior is proposed. Based on simulated and real datasets, we demonstrate that the proposed method performs better than five other competing methods in terms of variable selection and classification.

Keywords: Sparse Bayesian method; Multinomial probit model; Correlation prior; High-dimensional data classification (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1016/j.spl.2016.08.008

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