Variable fusion for Bayesian linear regression via spike-and-slab priors
This addresses variable fusion in linear regression for statistical modeling, but it appears incremental as it builds on existing spike-and-slab prior approaches.
The paper tackles the problem of identifying predictors with similar relationships to a response in linear regression, known as variable fusion, by proposing a novel Bayesian method using spike-and-slab priors, and simulation studies and real data analysis show it achieves better performance than previous methods.
In linear regression models, fusion of coefficients is used to identify predictors having similar relationships with a response. This is called variable fusion. This paper presents a novel variable fusion method in terms of Bayesian linear regression models. We focus on hierarchical Bayesian models based on a spike-and-slab prior approach. A spike-and-slab prior is tailored to perform variable fusion. To obtain estimates of the parameters, we develop a Gibbs sampler for the parameters. Simulation studies and a real data analysis show that our proposed method achieves better performance than previous methods.