On Regression in Extreme Regions

arXiv:2303.03084v2 Announce Type: replace-cross
Abstract: The statistical learning problem consists in building a predictive function $\hat{f}$ based on independent copies of $(X,Y)$ so that $Y$ is approximated by $\hat{f}(X)$ with minimum (squared) error. Motivated by various applications, special attention is paid here to the case of extreme (i.e. very large) observations $X$. Because of their rarity, the contributions of such observations to the (empirical) error is negligible, and the predictive performance of empirical risk minimizers can be consequently very poor …

abstract applications arxiv attention building case cs.lg error function independent math.st predictive regression statistical stat.ml stat.th type