【摘 要】
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For high-dimensional linear regression, both lo and l1 norms on the coefficients have been used for sparse modeling of the regression funetion.In this work,
【机 构】
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University of Minnesota 224 Church Street, Minneapolis, MN 55455
【出 处】
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The Third IMS-China International Conference on Statistics a
论文部分内容阅读
For high-dimensional linear regression, both lo and l1 norms on the coefficients have been used for sparse modeling of the regression funetion.In this work, we identify the minimax rates of convergence for regression estimation under lq constraints on the coefficients for 0 < q < 1 for both random and fixed designs.Furthermore,our estimators based on model combination/selection are showed to simultaneously achieve the optimal rates over the whole range of 0 ≤ q ≤ 1.Our results also permit model mis-specification.
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