The Asymptotic Approximation of EPMC for Linear Discriminant Rules Using a Moore-Penrose Inverse Matrix in High Dimension

DOI10.1080/03610926.2011.628768zbMath1416.62367OpenAlexW2011836525MaRDI QIDQ2862312

Takashi Seo, Masashi Hyodo, Takayuki Yamada

Publication date: 14 November 2013

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2011.628768

zbMATH Keywords

expected probability of misclassificationlinear discriminant rulehigh-dimensional approximationsmaximum likelihood classification rule

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Theory of matrix inversion and generalized inverses (15A09)

Related Items (1)

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Cites Work

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• Minimum distance classification rules for high dimensional data
• Improving implementation of linear discriminant analysis for the high dimension/small sample size problem
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Asymptotic expansion of the distribution of the Z statistic in discriminant analysis
• An Asymptotic Expansion for the Distribution of the Linear Discriminant Function

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