Classification of Observations into One of Two Artificially Dichotomized Classes by Using a Normal Screening Variable

DOI10.1080/03610920802271267zbMath1159.62046OpenAlexW2036534681MaRDI QIDQ3622076

Hea-Jung Kim

Publication date: 23 April 2009

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

Full work available at URL: https://doi.org/10.1080/03610920802271267

zbMATH Keywords

optimal error rateartificial dichotomizationdoubly truncated multivariate skew-normal distribution

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Hypothesis testing in multivariate analysis (62H15)

Related Items (4)

Segmented classification analysis with a class of rectangle-screened elliptical populations ⋮ An optimal classification rule for multiple interval-screened scale mixture of normal populations ⋮ A best linear threshold classification with scale mixture of skew normal populations ⋮ Best Linear Classification Rule for Multivariate Interval Screened Data

Uses Software

• PRMLT

Cites Work

• Discriminant analysis and clustering. Panel on discriminant analysis, classification, and clustering
• The Performance of Fisher's Linear Discriminant Function under Non-Optimal Conditions
• The multivariate skew-normal distribution
• A unified view on skewed distributions arising from selections
• A class of weighted normal distributions and its variants useful for inequality constrained analysis
• An Asymptotic Expansion for the Distribution of the Linear Discriminant Function
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