Adaptive spectral regularizations of high dimensional linear models

Abstract

This paper focuses on recovering an unknown vector $\beta$ from the noisy data $Y=X\beta +\sigma \xi$, where $X$ is a known $n\times p$-matrix, $\xi$ is a standard white Gaussian noise, and $\sigma$ is an unknown noise level. In order to estimate $\beta$, a spectral regularization method is used, and our goal is to choose its regularization parameter with the help of the data $Y$. In this paper, we deal solely with regularization methods based on the so-called ordered smoothers and provide some oracle inequalities in the case, where the noise level is unknown.

References in corpus (1)

• Risk hull method and regularization by projections of ill-posed inverse problems