High-dimensional analysis of ridge regression for non-identically distributed data with a variance profile

arXiv:2403.20200v1 Announce Type: cross
Abstract: High-dimensional linear regression has been thoroughly studied in the context of independent and identically distributed data. We propose to investigate high-dimensional regression models for independent but non-identically distributed data. To this end, we suppose that the set of observed predictors (or features) is a random matrix with a variance profile and with dimensions growing at a proportional rate. Assuming a random effect model, we study the predictive risk of the ridge estimator for linear regression …

abstract analysis arxiv context data distributed distributed data features independent linear linear regression math.pr math.st profile regression ridge set stat.me stat.ml stat.th type variance