On coding effects in regularized categorical regression

Julien Chiquet (1) , Yves Grandvalet (2) , Guillem Rigaill (3)

1 SG - Laboratoire Statistique et Génome
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
3 IPS2 (UMR_9213 / UMR_1403) - Institut des Sciences des Plantes de Paris-Saclay

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.

Mots clés: coding system; regularization; ordinal regression; sparsity; categorical predictor

Domaines: Statistiques [math.ST]

Julien Chiquet, Yves Grandvalet, Guillem Rigaill. On coding effects in regularized categorical regression. Statistical Modelling, 2016, 16 (3), pp.228-237. ⟨10.1177/1471082X16644998⟩. ⟨hal-01338164⟩