Regularising Generalised Linear Mixed Models with an autoregressive random effect
Résumé
We address regularised versions of the Expectation-Maximisation (EM) algorithm for Generalised Linear Mixed Models (GLMM) in the context of panel data (measured on several individuals at different time-points). A random response y is modelled by a GLMM, using a set X of explanatory variables and two random effects. The first one introduces the dependence within individuals on which data is repeatedly collected while the second one embodies the serially correlated time-specific effect shared by all the individuals. Variables in X are assumed many and redundant, so that regression demands regularisation. In this context, we first propose a L2-penalised EM algorithm, and then a supervised component-based regularised EM algorithm as an alternative.
Fichier principal
IWSM2017.pdf (834.88 Ko)
Télécharger le fichier
presIWSM2017.pdf (1.88 Mo)
Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Format : Présentation
Loading...