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arXiv · 1801.07012

Extension de la r\'egression lin\'eaire g\'en\'eralis\'ee sur composantes supervis\'ees (SCGLR) aux donn\'ees group\'ees

Jocelyn Chauvet·Catherine Trottier·Xavier Bry·Frédéric Mortier
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Abstract

We address component-based regularisation of a multivariate Generalized Linear Mixed Model. A set of random responses Y is modelled by a GLMM, using a set X of explanatory variables and a set T of additional covariates. Variables in X are assumed many and redundant: generalized linear mixed regression demands regularisation with respect to X. By contrast, variables in T are assumed few and selected so as to demand no regularisation. Regularisation is performed building an appropriate number of orthogonal components that both contribute to model Y and capture relevant structural information in X. We propose to optimize a SCGLR-specific criterion within a Schall's algorithm in order to estimate the model. This extension of SCGLR is tested on simulated and real data, and compared to Ridge-and Lasso-based regularisations.

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Jocelyn Chauvet, Catherine Trottier, Xavier Bry, Frédéric Mortier. 2018-01-22. Extension de la r\'egression lin\'eaire g\'en\'eralis\'ee sur composantes supervis\'ees (SCGLR) aux donn\'ees group\'ees. https://arxiv.org/abs/1801.07012

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