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stat.ME2025
Semi-supervised learning for linear extremile regression
Rong Jiang, Keming Yu, Jiangfeng Wang
Extremile regression, as a least squares analog of quantile regression, is potentially useful tool for modeling and understanding the extreme tails of a distribution. However, exis…
stat.ME2023
Root n consistent extremile regression and its supervised and semi-supervised learning
Rong Jiang, Keming Yu
Extremile (Daouia, Gijbels and Stupfler,2019) is a novel and coherent measure of risk, determined by weighted expectations rather than tail probabilities. It finds application in r…