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A supersaturated design (SSD) is a design whose run size is not enough for estimating all the main effects. An important goal in the analysis of such designs is to identify active effects based on the effect sparsity assumption. A Bayesian variable selection strategy which combines the advantages of the componentwise Gibbs sampler (see Chen et al., 2011) and the functionally induced priors (see Joseph...
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