Ordinal regression and classification methods form a vital branch of statistical learning wherein the outcome variable possesses an inherent order. Unlike conventional classification problems, where ...

Abstract: Ordinal regression addresses the problem of predicting non-numerical ordered classes. It walks a fine line between standard regression and classification, and the problem is often addressed ...

Introduction: We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) ...

This project demonstrates the use of Ordinal Logistic Regression and One-vs-Rest (OvR) Logistic Regression for clustering data points into classes. Ordinal Logistic Regression is particularly suitable ...

This article develops a methodology for regression analysis of ordinal response data subject to interval censoring. This work is motivated by the need to analyze data from multiple studies in ...

Researchers often use outcome-dependent sampling to study the exposure-outcome association. The case-control study is a widely used example of outcome-dependent sampling when the outcome is binary.