Regularising Generalised Linear Mixed Models with an autoregressive random effect
Abstract
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.
Keywords
Cite
@article{arxiv.1908.07477,
title = {Regularising Generalised Linear Mixed Models with an autoregressive random effect},
author = {Jocelyn Chauvet and Catherine Trottier and Xavier Bry},
journal= {arXiv preprint arXiv:1908.07477},
year = {2019}
}