ArticleJournal of applied statistics2026
Robust Bayesian model averaging for linear regression models with heavy-tailed errors.
Article in Journal of applied statistics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- MixedBayes: An R Package for Longitudinal Gene-Environment Interaction Analysis Using Robust Sparse Bayesian Mixed Models.Entropy (Basel, Switzerland) · 2026Article
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2 authors.
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Abstract
Our goal is to develop a Bayesian model averaging technique in linear regression models that accommodates heavier tailed error densities than the normal distribution. Motivated by the use of the Huber loss function in the presence of outliers, the Bayesian Huberized lasso with hyperbolic errors has been proposed and recently implemented in the literature. Since the Huberized lasso cannot enforce regression coefficients to be exactly zero, we propose a Bayesian variable selection approach with spike and slab priors to address sparsity more effectively. The shapes of the hyperbolic and the Student-
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