A penalized approach for the bivariate ordered logistic model with applications to social and medical data. (October 2019)

• Record Type: Journal Article
• Authors: Enea, Marco; Lovison, Gianfranco
• Is Part Of: Statistical modelling. Volume 19:Number 5(2019)
• Page Start: 467
• Page End: 500
• Publication Date: 2019-10
• Subjects: Dale model -- bivariate ordered logistic model -- penalized maximum likelihood estimation -- ordinal association; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X18782063
• Languages: English
• ISSNs: 1471-082X

Abstract

Bivariate ordered logistic models (BOLMs) are appealing to jointly model the marginal distribution of two ordered responses and their association, given a set of covariates. When the number of categories of the responses increases, the number of global odds ratios to be estimated also increases, and estimation gets problematic. In this work we propose a non-parametric approach for the maximum likelihood (ML) estimation of a BOLM, wherein penalties to the differences between adjacent row and column effects are applied. Our proposal is then compared to the Goodman and Dale models. Some simulation results as well as analyses of two real data sets are presented and discussed.