The linear and euclidean discriminant functions: a comparison v1a asymptotic expansions and simulation study

DOI10.1080/03610929608831882zbMath0900.62310OpenAlexW1987626982MaRDI QIDQ3125797

J. P. Koolaard, C. R. O. Lawoko

Publication date: 17 February 1998

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

Full work available at URL: https://doi.org/10.1080/03610929608831882

zbMATH Keywords

asymptotic expansionerror ratelinear discriminant functionEuclidean distance classifier

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Monte Carlo methods (65C05)

Related Items (4)

Results in statistical discriminant analysis: A review of the former Soviet Union literature. ⋮ An asymptotic expansion for the distribution of Euclidean distance-based discriminant function in normal populations ⋮ Sampling strategy for optimal classification into one of two correlated normal populations ⋮ The use of a distance measure in regularised discriminant analysis

Cites Work

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• Covariance pooling and stabilization for classification
• Partially pooled covariance matrix estimation in discriminant analysis
• The Euclidean distance classifier: an alternative to the linear discriminant function
• On the Effects of Dimension in Discriminant Analysis for Unequal Covariance Populations
• On Dimensionality, Sample Size, Classification Error, and Complexity of Classification Algorithm in Pattern Recognition
• The asymptotic distributions of the conditional error rate and risk in discriminant analysis
• AN ASYMPTOTIC EXPANSION OF THE EXPECTATION OF THE ESTIMATED ERROR RATE IN DISCRIMINANT ANALYSIS1
• An Asymptotic Unbiased Technique for Estimating the Error Rates in Discriminant Analysis
• Correction Notes: Correction to "An Asymptotic Expansion for the Distribution of the Linear Discriminant Function"
• AN ASYMPTOTIC EXPANSION FOR THE VARIANCE OF THE ERRORS OF MISCLASSIFICATION OF THE LINEAR DISCRIMINANT FUNCTION¹

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