Alternative linear classification rules under order restrictions

DOI10.1080/03610929808832113zbMath0895.62064OpenAlexW2008576235MaRDI QIDQ4386459

Tianyong Long, Rameshwar D. Gupta

Publication date: 26 April 1998

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

Full work available at URL: https://doi.org/10.1080/03610929808832113

zbMATH Keywords

misclassification probabilityorder restrictionsoptimal classification ruleexpected cost of misclassificationAnderson's classification rule

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (9)

Bayes discriminant rules with ordered predictors ⋮ Ordered classification rules for inverse gaussian populations with unknown parameters ⋮ Estimation and classification using progressive type-II censored samples from two exponential populations with a common location ⋮ Classification rules for two exponential populations with common location and ordered scales under censored samples ⋮ Bayesian estimation and classification for two logistic populations with a common location ⋮ Unnamed Item ⋮ Classification rules for two parameter exponential populations under order restrictions on parameters ⋮ Robustness of classification rules that incorporate additional information ⋮ A classification rule for ordered exponential populations

Cites Work

• On estimation of discriminant coefficients
• Quadratic loss of order restricted estimators for treatment means with a control
• On the estimation of restricted mean
• The quadratic loss of isotonic regression under normality
• Discriminant analysis and clustering. Panel on discriminant analysis, classification, and clustering
• On linear discriminant analysis with adaptive ridge classification rules
• Penalized discriminant analysis
• Biased discriminant analysis: Evaluation of the optimum probability of misclassification
• On linear lon-odds and estimation of discriminant coefficients
• The application of bias to discriminant analysis
• The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis
• Optimum Classification Rules for Classification into Two Multivariate Normal Populations

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