Improving precipitation forecasts using extreme quantile regression

DOI10.1007/s10687-019-00355-1zbMath1434.62058arXiv1806.05429OpenAlexW2963483783MaRDI QIDQ2283052

Geurt Jongbloed, Maurice Schmeits, Juan-Juan Cai, Jasper Velthoen

Publication date: 27 December 2019

Published in: Extremes (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.05429

zbMATH Keywords

asymptoticsextreme conditional quantilelocal linear quantile regressionextreme precipitationforecast skillstatistical post-processing

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12)

Related Items (4)

Gradient boosting for extreme quantile regression ⋮ Optimal weighted pooling for inference about the tail index and extreme quantiles ⋮ Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models ⋮ Scoring predictions at extreme quantiles

Cites Work

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