Prediction of extremal precipitation by quantile regression forests: from SNU multiscale team

DOI10.1007/s10687-018-0323-yzbMath1410.62074OpenAlexW2800659164WikidataQ129848634 ScholiaQ129848634MaRDI QIDQ1792636

Seoncheol Park, Junhyeon Kwon, Hee-Seok Oh, Joonpyo Kim

Publication date: 12 October 2018

Published in: Extremes (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10687-018-0323-y

zbMATH Keywords

predictionquantile regression forestscircular transformextreme precipitationspatio-temporal extremes

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Nonparametric regression and quantile regression (62G08) Applications of statistics to environmental and related topics (62P12) Statistics of extreme values; tail inference (62G32)

Uses Software

• astsa
• ElemStatLearn

Cites Work

• Unnamed Item
• A spatio-temporal dynamic regression model for extreme wind speeds
• Extremal quantile regression
• Consistency of random forests
• Time Series Analysis and Its Applications
• Random forests

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