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

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  • Yang, Aijun
  • Jiang, Xuejun
  • Liu, Pengfei
  • Lin, Jinguan

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.

Suggested Citation

  • Yang, Aijun & Jiang, Xuejun & Liu, Pengfei & Lin, Jinguan, 2016. "Sparse Bayesian multinomial probit regression model with correlation prior for high-dimensional data classification," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 241-247.
    • Handle: RePEc:eee:stapro:v:119:y:2016:i:c:p:241-247
    DOI: 10.1016/j.spl.2016.08.008
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    References listed on IDEAS

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    1. Aijun Yang & Yunxian Li & Niansheng Tang & Jinguan Lin, 2015. "Bayesian variable selection in multinomial probit model for classifying high-dimensional data," Computational Statistics, Springer, vol. 30(2), pages 399-418, June.
    2. Yuan, Ming & Lin, Yi, 2005. "Efficient Empirical Bayes Variable Selection and Estimation in Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1215-1225, December.
    3. Naijun Sha & Marina Vannucci & Mahlet G. Tadesse & Philip J. Brown & Ilaria Dragoni & Nick Davies & Tracy C. Roberts & Andrea Contestabile & Mike Salmon & Chris Buckley & Francesco Falciani, 2004. "Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage," Biometrics, The International Biometric Society, vol. 60(3), pages 812-819, September.
    4. Jerome H. Friedman & Jacqueline J. Meulman, 2004. "Clustering objects on subsets of attributes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 815-849, November.
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