Design of $c$-Optimal Experiments for High dimensional Linear Models
Statistics Theory
2020-10-27 v1 Methodology
Statistics Theory
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.
Cite
@article{arxiv.2010.12580,
title = {Design of $c$-Optimal Experiments for High dimensional Linear Models},
author = {Hamid Eftekhari and Moulinath Banerjee and Ya'acov Ritov},
journal= {arXiv preprint arXiv:2010.12580},
year = {2020}
}