Abstract:Sparseness of the regression coefficient vector is often a desirable property, because, among other benefits, sparseness improves interpretability. In practice, many true regression coefficients might be negligibly small, but nonzero, which we refer to as quasi‐sparseness. Spike‐and‐slab priors can be tuned to ignore very small regression coefficients and, as a consequence, provide a trade‐off between prediction accuracy and interpretability. However, spike‐and‐slab priors with full support lead to inconsisten… Show more
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