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Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects

Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects

ABSTRACT Modern biomedical datasets are increasingly high-dimensional and exhibit complex correlation structures. Generalized linear mixed models (GLMMs) have long been employed to account for such dependencies. However, proper specification of the fixed and random effects in GLMMs is increasingly difficult in high dimensions, and computational complexity grows with increasing dimension …