Robust Linear Regression for General Feature Distribution

no code implementations • 4 Feb 2022 • Tom Norman, Nir Weinberger, Kfir Y. Levy

In this work we go beyond these assumptions and investigate robust regression under a more general set of assumptions: $\textbf{(i)}$ we allow the covariance matrix to be either positive definite or positive semi definite, $\textbf{(ii)}$ we do not necessarily assume that the features are centered, $\textbf{(iii)}$ we make no further assumption beyond boundedness (sub-Gaussianity) of features and measurement noise.

regression