Design of $c$-Optimal Experiments for High dimensional Linear Models

Hamid Eftekhari; Moulinath Banerjee; Ya'acov Ritov

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arXiv · 2010.12580

Design of $c$-Optimal Experiments for High dimensional Linear Models

Hamid Eftekhari·Moulinath Banerjee·Ya'acov Ritov

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Abstract

We study random designs that minimize the asymptotic variance of a de-biased lasso estimator when a large pool of unlabeled data is available but measuring the corresponding responses is costly. The optimal sampling distribution arises as the solution of a semidefinite program. The improvements in efficiency that result from these optimal designs are demonstrated via simulation experiments.

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Hamid Eftekhari, Moulinath Banerjee, Ya'acov Ritov. 2020-10-23. Design of $c$-Optimal Experiments for High dimensional Linear Models. https://arxiv.org/abs/2010.12580

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