A penalized likelihood estimator for GEV parameters, weighted by generalized random forest weights, is introduced for extreme quantile regression to improve tail extrapolation and handle many predictors.
Machine learning45, 5–32 (2001)
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Penalized estimation of GEV parameters for extreme quantile regression
A penalized likelihood estimator for GEV parameters, weighted by generalized random forest weights, is introduced for extreme quantile regression to improve tail extrapolation and handle many predictors.