Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/244528 
Year of Publication: 
2017
Series/Report no.: 
Working Paper No. 6/2017
Publisher: 
Örebro University School of Business, Örebro
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 coefficient 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.
Subjects: 
discriminant function
stochastic representation
large-dimensional asymptotics
random matrix theory
classication analysis
JEL: 
C12
C13
C44
Document Type: 
Working Paper

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