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A consistent variable selection method in high-dimensional canonical discriminant analysis

Author

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  • Oda, Ryoya
  • Suzuki, Yuya
  • Yanagihara, Hirokazu
  • Fujikoshi, Yasunori

Abstract

In this paper, we obtain the sufficient conditions to determine the consistency of a variable selection method based on a generalized information criterion in canonical discriminant analysis. To examine the consistency property, we use a high-dimensional asymptotic framework such that as the sample size n goes to infinity, then the ratio of the length of the observation vector p to the sample size, p∕n, converges to a constant that is less than one even if the dimension of the observation vector also goes to infinity. Using the derived conditions, we propose a consistent variable selection method. From numerical simulations, we show that the probability of selecting the true model by our proposed method is high even when p is large. Further, the advantage of the proposed method is demonstrated by a real data.

Suggested Citation

  • Oda, Ryoya & Suzuki, Yuya & Yanagihara, Hirokazu & Fujikoshi, Yasunori, 2020. "A consistent variable selection method in high-dimensional canonical discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:jmvana:v:175:y:2020:i:c:s0047259x19300545
    DOI: 10.1016/j.jmva.2019.104561
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    References listed on IDEAS

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    1. Irina Gaynanova & James G. Booth & Martin T. Wells, 2016. "Simultaneous Sparse Estimation of Canonical Vectors in the ≫ Setting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 696-706, April.
    2. 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.
    3. Fujikoshi, Yasunori & Sakurai, Tetsuro, 2016. "High-dimensional consistency of rank estimation criteria in multivariate linear model," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 199-212.
    4. Zhao, L. C. & Krishnaiah, P. R. & Bai, Z. D., 1986. "On detection of the number of signals in presence of white noise," Journal of Multivariate Analysis, Elsevier, vol. 20(1), pages 1-25, October.
    5. 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.
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    Cited by:

    1. Yehan Ma & Arthur B. Yeh & John T. Chen, 2023. "Simultaneous Confidence Regions and Weighted Hypotheses on Parameter Arrays," Methodology and Computing in Applied Probability, Springer, vol. 25(2), pages 1-18, June.
    2. Nakagawa, Tomoyuki & Watanabe, Hiroki & Hyodo, Masashi, 2021. "Kick-one-out-based variable selection method for Euclidean distance-based classifier in high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    3. Aleksey I. Shinkevich & Alsu R. Akhmetshina & Ruslan R. Khalilov, 2022. "Development of a Methodology for Forecasting the Sustainable Development of Industry in Russia Based on the Tools of Factor and Discriminant Analysis," Mathematics, MDPI, vol. 10(6), pages 1-16, March.
    4. Yasunori Fujikoshi & Tetsuro Sakurai, 2023. "High-Dimensional Consistencies of KOO Methods for the Selection of Variables in Multivariate Linear Regression Models with Covariance Structures," Mathematics, MDPI, vol. 11(3), pages 1-15, January.
    5. Fujikoshi, Yasunori, 2022. "High-dimensional consistencies of KOO methods in multivariate regression model and discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 188(C).

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