Maximizing proportions of correct classifications in binary logistic regression

DOI10.1080/02664760600723367zbMath1118.62335OpenAlexW2002133559MaRDI QIDQ3592603

Petros Hadjicostas

Publication date: 13 September 2007

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664760600723367

zbMATH Keywords

sensitivityclassificationlogistic regressionspecificityconcordant pairsdiscordant pairscut-off pointsmaximization of proportions

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Generalized linear models (logistic models) (62J12)

Uses Software

• MINITAB
• SAS
• SAS/STAT

Cites Work

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• Maximum likelihood estimates in exponential response models
• Logistic Regression, a review
• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• How Biased is the Apparent Error Rate of a Prediction Rule?
• Computing Distributions for Exact Logistic Regression
• The General Distribution of the Error Rate of a Classification Procedure with Application to Logistic Regression Discrimination
• Error rates of non-Bayes classification rules and the robustness of Fisher's linear discriminant function
• The Bias of Estimating Equations with Application to the Error Rate of Logistic Discrimination
• Linear Model Selection by Cross-Validation
• The asymptotic distribution of the proportion of correct classifications for a holdout sample in logistic regression

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