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Sparse bayesian kernel multinomial probit regression model for high-dimensional data classification

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  • Aijun Yang
  • Xuejun Jiang
  • Lianjie Shu
  • Pengfei Liu

Abstract

In this paper we introduce a sparse Bayesian kernel multinomial probit regression model for multi-class cancer classification. The relationship between the cancer types and gene expression measurements is explained by an unknown function which belongs to an abstract functional space like the reproducing kernel Hilbert space. We assign a sparse prior for regression parameters and perform variable selection by indexing the covariates of the model with a binary vector. The correlation prior for the binary vector assigned in this paper is able to distinguish models with the same size. The proposed method is successfully tested on one simulated data set and two publicly available real life data sets.

Suggested Citation

  • Aijun Yang & Xuejun Jiang & Lianjie Shu & Pengfei Liu, 2019. "Sparse bayesian kernel multinomial probit regression model for high-dimensional data classification," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(1), pages 165-176, January.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:1:p:165-176
    DOI: 10.1080/03610926.2018.1463385
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    Cited by:

    1. Cheng, Xiu & Wu, Fan & Long, Ruyin & Li, Wenbo, 2021. "Uncovering the effects of learning capacity and social interaction on the experienced utility of low-carbon lifestyle guiding policies," Energy Policy, Elsevier, vol. 154(C).

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