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.
American journal of epidemiology180(3), 330–331 (2014) 20 Figure D4 Variation of the parametersˆµ(x), ˆσ(x), and ˆξ(x) as a function of age
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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.