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Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification

Author

Listed:
  • Yang Aijun
  • Jiang Xuejun
  • Xiang Liming
  • Lin Jinguan

Abstract

Here we consider a multinomial probit regression model where the number of variables substantially exceeds the sample size and only a subset of the available variables is associated with the response. Thus selecting a small number of relevant variables for classification has received a great deal of attention. Generally when the number of variables is substantial, sparsity-enforcing priors for the regression coefficients are called for on grounds of predictive generalization and computational ease. In this paper, we propose a sparse Bayesian variable selection method in multinomial probit regression model for multi-class classification. The performance of our proposed method is demonstrated with one simulated data and three well-known gene expression profiling data: breast cancer data, leukemia data, and small round blue-cell tumors. The results show that compared with other methods, our method is able to select the relevant variables and can obtain competitive classification accuracy with a small subset of relevant genes.

Suggested Citation

  • Yang Aijun & Jiang Xuejun & Xiang Liming & Lin Jinguan, 2017. "Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(12), pages 6137-6150, June.
  • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:12:p:6137-6150
    DOI: 10.1080/03610926.2015.1122056
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