On coding effects in regularized categorical regression. (June 2016)

• Record Type: Journal Article
• Authors: Chiquet, Julien; Grandvalet, Yves; Rigaill, Guillem
• Is Part Of: Statistical modelling. Volume 16:Number 3(2016)
• Page Start: 228
• Page End: 237
• Publication Date: 2016-06
• Subjects: categorical predictor -- Ordinal regression -- regularization -- coding system -- sparsity; 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: 10.1177/1471082X16644998
• Languages: English
• ISSNs: 1471-082X

Abstract

This discussion is a continuation of Tutz and Gertheiss (2016)'s paper, where we focus on the importance of the coding of effects in regularized categorical and ordinal regression. We show that, though that an appropriate regularization is profitable for any coding, the choice of a relevant coding can prevail over the one of the regularization term for revealing structures. We focus on predictors though the issues raised also apply to responses. We illustrate our point on a classic data set.