Maximizing proportions of correct classifications in binary logistic regression
From MaRDI portal
Publication:3592603
DOI10.1080/02664760600723367zbMath1118.62335OpenAlexW2002133559MaRDI QIDQ3592603
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
sensitivityclassificationlogistic regressionspecificityconcordant pairsdiscordant pairscut-off pointsmaximization of proportions
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Generalized linear models (logistic models) (62J12)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- 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
This page was built for publication: Maximizing proportions of correct classifications in binary logistic regression