Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques
Griesbach C, Groll A, Bergherr E (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 16
Article Number: e0254178
Journal Issue: 7 July
DOI: 10.1371/journal.pone.0254178
Abstract
Boosting techniques from the field of statistical learning have grown to be a popular tool for estimating and selecting predictor effects in various regression models and can roughly be separated in two general approaches, namely gradient boosting and likelihood-based boosting. An extensive framework has been proposed in order to fit generalized mixed models based on boosting, however for the case of cluster-constant covariates likelihood-based boosting approaches tend to mischoose variables in the selection step leading to wrong estimates. We propose an improved boosting algorithm for linear mixed models, where the random effects are properly weighted, disentangled from the fixed effects updating scheme and corrected for correlations with cluster-constant covariates in order to improve quality of estimates and in addition reduce the computational effort. The method outperforms current state-of-the-art approaches from boosting and maximum likelihood inference which is shown via simulations and various data examples.
Authors with CRIS profile
Colin Griesbach
Lehrstuhl für Biometrie und Epidemiologie
Elisabeth Bergherr
Lehrstuhl für Biometrie und Epidemiologie
Involved external institutions
Germany (DE)How to cite
APA:
Griesbach, C., Groll, A., & Bergherr, E. (2021). Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques. PLoS ONE, 16(7 July). https://doi.org/10.1371/journal.pone.0254178
MLA:
Griesbach, Colin, Andreas Groll, and Elisabeth Bergherr. "Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques." PLoS ONE 16.7 July (2021).
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