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Discriminant analysis on high dimensional Gaussian copula model

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  • He, Yong
  • Zhang, Xinsheng
  • Wang, Pingping

Abstract

In this paper we propose a classifier for the high dimensional Gaussian copula model. Besides, a Rotate-and-Solve procedure is proposed to tackle with the non-sparse case. Both theoretical analysis and simulation study show that the classifier performs better than some state-of-the-art methods.

Suggested Citation

  • He, Yong & Zhang, Xinsheng & Wang, Pingping, 2016. "Discriminant analysis on high dimensional Gaussian copula model," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 100-112.
    • Handle: RePEc:eee:stapro:v:117:y:2016:i:c:p:100-112
    DOI: 10.1016/j.spl.2016.05.018
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    References listed on IDEAS

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    1. Trendafilov, Nickolay T. & Jolliffe, Ian T., 2007. "DALASS: Variable selection in discriminant analysis via the LASSO," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3718-3736, May.
    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. Ning Hao & Bin Dong & Jianqing Fan, 2015. "Sparsifying the Fisher linear discriminant by rotation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(4), pages 827-851, September.
    4. Jianqing Fan & Yang Feng & Xin Tong, 2012. "A road to classification in high dimensional space: the regularized optimal affine discriminant," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(4), pages 745-771, September.
    5. Patrick Danaher & Pei Wang & Daniela M. Witten, 2014. "The joint graphical lasso for inverse covariance estimation across multiple classes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(2), pages 373-397, March.
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    Cited by:

    1. He, Yong & Zhang, Xinsheng & Zhang, Liwen, 2018. "Variable selection for high dimensional Gaussian copula regression model: An adaptive hypothesis testing procedure," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 132-150.
    2. Timothy I. Cannings & Richard J. Samworth, 2017. "Random-projection ensemble classification," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 959-1035, September.

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