A spike-and-slab Bayesian approach with tailored nonlocal priors enables simultaneous variable fusion and selection in linear regression within the BMA framework using Gibbs sampling.
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Variable Fusion and Selection via a Spike-and-Slab Approach with Nonlocal Priors
A spike-and-slab Bayesian approach with tailored nonlocal priors enables simultaneous variable fusion and selection in linear regression within the BMA framework using Gibbs sampling.