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School of Business, Örebro University Working Papers, School of Business, Örebro University

No 2017:6:
Discriminant analysis in small and large dimensions

Taras Bodnar (), Stepan Mazur (), Edward Ngailo () and Nestor Parolya ()

Abstract: In this article we study the distributional properties of the linear discriminant function under the assumption of the normality by comparing two groups with the same covariance matrix but di erent mean vectors. A stochastic representation of the discriminant function coecient is derived which is then used to establish the asymptotic distribution under the high-dimensional asymptotic regime. Moreover, we investigate the classi cation analysis based on the discriminant function in both small and large dimensions. In the numerical study, a good nite-sample perfor- mance of the derived large-dimensional asymptotic distributions is documented.

Keywords: discriminant function; stochastic representation; large-dimensional asymptotics; random matrix theory; classication analysis; (follow links to similar papers)

JEL-Codes: C12; C13; C44; (follow links to similar papers)

27 pages, August 22, 2017

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