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Variable Selection in High-Dimensional Generalized Linear Mixed Models for Binary Data
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DOI：10.1007/s12561-025-09509-1.png)
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Abstract
En 中文
High-dimensional data are now routinely analyzed, typically with many more covariates than observations, and are often solved using computationally expensive Markov Chain Monte Carlo techniques or very complicated optimization algorithms. An alternative method for regularizing selected variables is the Lasso, which is widely applied to generalized linear models, where fast and efficient methods based on the gradient descent algorithm are available. In this paper, we propose an approach that combines the expectation-maximization algorithm and the popular Lasso for variable selection of fixed effects in Bernoulli generalized linear mixed effects models under probit and logit links. We perform extensive simulation studies to show the computational advantage of our method compared to alternative approach available in the literature for binary responses related to its power to identify significant predictors and asymptotically consistent estimators. The application shows the performance of the proposal procedure in handling predictors of different types and cumulative noise.
Keywords:
Correlated binary data
EM algorithm
Generalized linear mixed models
High-dimensional data
Truncated multivariate normal distribution
Glmnet
Journal
S
IF:
0.4
Papers:
22
Citations:
360