Hierarchical selection of fixed and random effects in generalized linear mixed models
In many applications of generalized linear mixed models(GLMMs), there is a hierarchical structure in the effects that needs to be taken into account when performing variable selection. A prime example of this is when fitting mixed models to longitudinal data, where it is usual for covariates to be included as only fixed effects or as composite (fixed and random) effects. In this article, we propose the first regularization method that can deal with large numbers of candidate GLMMs while...[Show more]
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|ms-crepe-supp-ssedited.pdf||Supplementary Material||270.62 kB||Adobe PDF|
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