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Discriminant analysis in small and large dimensions
Department of Mathematics, Stockholm University, Stockholm, Sweden.
Örebro University, Örebro University School of Business. Unit of Statistics.
Department of Mathematics, Stockholm University, Stockholm, Sweden.
Institute of Empirical Economics, Leibniz University of Hannover, Hannover, Germany.
2017 (English)Manuscript (preprint) (Other academic)
Abstract [en]

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 different 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 classification analysis based on the discriminant function in both small and large dimensions. In the numerical study, a good finite-sample performance of the derived large-dimensional asymptotic distributions is documented.

Place, publisher, year, edition, pages
2017. , 27 p.
Keyword [en]
discriminant function, stochastic representation, large-dimensional asymp- totics, random matrix theory, classification analysis
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:oru:diva-61248OAI: oai:DiVA.org:oru-61248DiVA: diva2:1146951
Available from: 2017-10-04 Created: 2017-10-04 Last updated: 2017-10-05Bibliographically approved

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Mazur, Stepan

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