Spike-and-slab regression
Bayesian variable selection technique in statistics / From Wikipedia, the free encyclopedia
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Spike-and-slab regression is a type of Bayesian linear regression in which a particular hierarchical prior distribution for the regression coefficients is chosen such that only a subset of the possible regressors is retained. The technique is particularly useful when the number of possible predictors is larger than the number of observations.[1] The idea of the spike-and-slab model was originally proposed by Mitchell & Beauchamp (1988).[2] The approach was further significantly developed by Madigan & Raftery (1994)[3] and George & McCulloch (1997).[4] A recent and important contribution to this literature is Ishwaran & Rao (2005).[5]