 Spike-and-slab regression

In statistics, spike-and-slab regression is a Bayesian variable selection technique that is particularly useful when the number of possible predictors is larger than the number of observations.Initially, the idea of the spike-and-slab model was proposed by Mitchell & Beauchamp (1988).