Regularising Generalised Linear Mixed Models with an autoregressive random effect

Jocelyn Chauvet (1) , Catherine Trottier (1, 2) , Xavier Bry (1)

1 IMAG - Institut Montpelliérain Alexander Grothendieck
2 UPVM - Université Paul-Valéry - Montpellier 3

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.

Mots clés: Regularised EM algorithm; Generalised Linear Mixed Model; Autoregressive random effect; Panel data analysis

Domaines: Statistiques [stat]; Méthodologie [stat.ME]; Applications [stat.AP]; Machine Learning [stat.ML]

Jocelyn Chauvet, Catherine Trottier, Xavier Bry. Regularising Generalised Linear Mixed Models with an autoregressive random effect. IWSM 2017, 32nd International Workshop on Statistical Modelling, Jul 2017, Groningen, Netherlands. ⟨hal-01818532⟩