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

Ryoya Oda, Yuya Suzuki, Hirokazu Yanagihara and Yasunori Fujikoshi

Journal of Multivariate Analysis, 2020, vol. 175, issue C

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.

Keywords: Consistency; Variable selection; Canonical discriminant analysis (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.jmva.2019.104561

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