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

JOURNAL OF MULTIVARIATE ANALYSIS

卷:175

Article

Oda, Ryoya¹  Suzuki, Yuya¹  Yanagihara, Hirokazu¹  Fujikoshi, Yasunori¹ 

[1] Hiroshima Univ, Grad Sch Sci, Dept Math, 1-3-1 Kagamiyama, Hiroshima 7398526, Japan

关键词: Consistency;    Variable selection;    Canonical discriminant analysis;   

DOI  :  10.1016/j.jmva.2019.104561

来源: Elsevier

【 摘 要 】

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. (C) 2019 Elsevier Inc. All rights reserved.

【 授权许可】

Free