Work

Regularising Generalised Linear Mixed Models with an autoregressive random effect

Year: 2019

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1908.07477

Authors: Chauvet, Jocelyn, Trottier, Catherine, Bry, Xavier

Institutions: Centre National de la Recherche Scientifique, Institut Montpelliérain Alexander Grothendieck, Université Montpellier 2, Université Paul-Valéry Montpellier

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2969475879

Cites: 8

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Related to: 10