Inference Without Compatibility
Statistics Theory
2020-01-23 v2 Statistics Theory
Abstract
We consider hypotheses testing problems for three parameters in high-dimensional linear models with minimal sparsity assumptions of their type but without any compatibility conditions. Under this framework, we construct the first -consistent estimators for low-dimensional coefficients, the signal strength, and the noise level. We support our results using numerical simulations and provide comparisons with other estimators.
Cite
@article{arxiv.1903.06295,
title = {Inference Without Compatibility},
author = {Michael Law and Ya'acov Ritov},
journal= {arXiv preprint arXiv:1903.06295},
year = {2020}
}