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 of the random effects. We present a novel reformulation of the GLMM using a factor model decomposition of the random effects, enabling scalable computation of GLMMs in high dimensions by reducing the latent space from a large number of random effects to a smaller set of latent factors. We also extend our prior work to estimate model parameters using a modified Monte Carlo Expectation Conditional Minimization algorit

SUBMITTER: Heiling HM 

PROVIDER: S-EPMC10946237 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature