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.
Sur la distribution limite du terme maximum d’une s´ erie al´ eatoire
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A consistent bias-corrected estimator based on blockwise top-two order statistics is developed for extreme value analysis after showing the naive independence-likelihood approach is inconsistent.
Excessively long blocks lower asymptotic relative efficiency in the block-maxima method, and new likelihood and diagnostic procedures are proposed to check whether a chosen length is adequate under rounding or censoring.
citing papers explorer
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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.
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Extreme Value Analysis based on Blockwise Top-Two Order Statistics
A consistent bias-corrected estimator based on blockwise top-two order statistics is developed for extreme value analysis after showing the naive independence-likelihood approach is inconsistent.
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How long should a block be?
Excessively long blocks lower asymptotic relative efficiency in the block-maxima method, and new likelihood and diagnostic procedures are proposed to check whether a chosen length is adequate under rounding or censoring.