  We study minimax rates for high-dimensional linear regression with additive
errors under the $\ell_p\ (1\leq p<\infty)$-losses, where the regression
parameter is of weak sparsity. Our lower and upper bounds agree up to constant
factors, implying that the proposed estimator is minimax optimal.
