High dimensional regression and matrix estimation without tuning parameters
Statistics Theory
2015-12-01 v3 Probability
Statistics Theory
Abstract
A general theory for Gaussian mean estimation that automatically adapts to unknown sparsity under arbitrary norms is proposed. The theory is applied to produce adaptively minimax rate-optimal estimators in high dimensional regression and matrix estimation that involve no tuning parameters.
Cite
@article{arxiv.1510.07294,
title = {High dimensional regression and matrix estimation without tuning parameters},
author = {Sourav Chatterjee},
journal= {arXiv preprint arXiv:1510.07294},
year = {2015}
}
Comments
23 pages, 1 figure. Minor corrections in this revision