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.
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Contributor : Catherine Trottier <>
Submitted on : Wednesday, August 7, 2019 - 12:41:05 PM
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  • HAL Id : hal-01818532, version 1

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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⟩

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