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We provide theoretical support into the suggested methodology by showing minimax optimality of the posterior suggest beneath the prediction risk in ultra-high dimensional settings where in fact the wide range of predictors can develop sub-exponentially relative to the test size. A one-step post-processing scheme caused by group lasso penalties in the rows for the predicted coefficient matrix is proposed for adjustable choice, with
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