Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO

Understand

High-dimensional data sets have become ubiquitous in the past few decades, often with many more covariates than observations.

• In the frequentist setting, penalized likelihood methods are the most popular approach for variable selection and estimation in high-dimensional data.
• In the Bayesian framework, spike-and-slab methods are commonly used as probabilistic constructs for high-dimensional modeling.
• Within the context of linear regression, Rockova and George (2018) introduced the spike-and-slab LASSO (SSL), an approach based on a prior which provides a continuum between the penalized likelihood LASSO and the Bayesian point-mass spike-and-slab formulations.