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High dimensional discrimination analysis via a semiparametric model

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  • Jiang, Binyan
  • Leng, Chenlei

Abstract

We propose a semiparametric linear programming discriminant (SLPD) rule for high dimensional discriminant analysis under a semiparametric model. As an extension, we further propose a two-stage SLPD (TSLPD) rule, which can have better classification performance under mild sparsity assumptions.

Suggested Citation

  • Jiang, Binyan & Leng, Chenlei, 2016. "High dimensional discrimination analysis via a semiparametric model," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 103-110.
    • Handle: RePEc:eee:stapro:v:110:y:2016:i:c:p:103-110
    DOI: 10.1016/j.spl.2015.11.012
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    References listed on IDEAS

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    1. Aneiros, Germán & Vieu, Philippe, 2014. "Variable selection in infinite-dimensional problems," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 12-20.
    2. Qing Mai & Hui Zou & Ming Yuan, 2012. "A direct approach to sparse discriminant analysis in ultra-high dimensions," Biometrika, Biometrika Trust, vol. 99(1), pages 29-42.
    3. Y. Lin, 2003. "Discriminant analysis through a semiparametric model," Biometrika, Biometrika Trust, vol. 90(2), pages 379-392, June.
    4. Germán Aneiros & Philippe Vieu, 2015. "Partial linear modelling with multi-functional covariates," Computational Statistics, Springer, vol. 30(3), pages 647-671, September.
    5. Wang, Cheng & Cao, Longbing & Miao, Baiqi, 2013. "Optimal feature selection for sparse linear discriminant analysis and its applications in gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 140-149.
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