On estimation of the noise variance in high-dimensional linear models
Statistics Theory
2017-11-28 v1 Statistics Theory
Abstract
We consider the problem of recovering the unknown noise variance in the linear regression model. To estimate the nuisance (a vector of regression coefficients) we use a family of spectral regularisers of the maximum likelihood estimator. The noise estimation is based on the adaptive normalisation of the squared error. We derive the upper bound for the concentration of the proposed method around the ideal estimator (the case of zero nuisance).
Keywords
Cite
@article{arxiv.1711.09208,
title = {On estimation of the noise variance in high-dimensional linear models},
author = {Yuri Golubev and Ekaterina Krymova},
journal= {arXiv preprint arXiv:1711.09208},
year = {2017}
}
Comments
in Russian