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 regularised versions of the Expectation-Maximisation (EM)algorithm for Generalised Linear Mixed Models (GLMM) in the context of paneldata (measured on several individuals at different time-points). A randomresponse y is modelled by a GLMM, using a set X of explanatory variables andtwo random effects. The first one introduces the dependence within individualson which data is repeatedly collected while the second one embodies theserially correlated time-specific effect shared by all the individuals.Variables in X are assumed many and redundant, so that regression demandsregularisation. In this context, we first propose a L2-penalised EM algorithm,and then a supervised component-based regularised EM algorithm as analternative.

Regularising Generalised Linear Mixed Models with an autoregressive random effect