Monte Carlo study of forward stepwise discrimination based on small samples

DOI10.1016/0898-1221(86)90077-5zbMath0607.62072OpenAlexW1981237938MaRDI QIDQ3746707

Tadashi Ashikaga, M. C. Costanza

Publication date: 1986

Published in: Computers & Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0898-1221(86)90077-5

zbMATH Keywords

small sampleslinear discriminant functionMonte Carlo studiesMahalanobis distancesF testsforward stepwise discriminationforward subset selection proceduresoptimal probability of correct classificationtwo group, P-variate normal classification problem

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Probabilistic methods, stochastic differential equations (65C99)

Related Items (2)

Bias in error rate estimates in discriminant analysis when stepwise variable selection is employed ⋮ Results in statistical discriminant analysis: A review of the former Soviet Union literature.

Uses Software

• SPSS
• SAS
• BMDP

Cites Work

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• A Note on the Generation of Random Normal Deviates
• Comparison of Stopping Rules in Forward Stepwise Discriminant Analysis
• Bias in Wilks' Λ in Stepwise Discriminant Analysis
• Robustness of Fisher's Linear Discriminant Function Under Two-Component Mixed Normal Models
• Discriminant Analysis
• Some Expected Values for Probabilities of Correct Classification in Discriminant Analysis
• On a Statistical Problem Arising in the Classification of an Individual into One of Two Groups
• Some Statistical Problems in Relating Experimental Data to Predicting Performance of a Production Process

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