English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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