Prediction of extremal precipitation by quantile regression forests: from SNU multiscale team
type: Item
instance of: scholarly article
title: Prediction of extremal precipitation by quantile regression forests: from SNU multiscale team
zbMATH Open document ID: 1410.62074
author: Seoncheol Park
author: Junhyeon Kwon
author: Joonpyo Kim
author: Hee-Seok Oh
published in: Extremes
publication date: 2018-10-12T00:00:00Z
Mathematics Subject Classification ID: 62G32
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62P12
zbMATH DE Number: 6952744
zbMATH Keywords: circular transform
zbMATH Keywords: extreme precipitation
zbMATH Keywords: prediction
zbMATH Keywords: quantile regression forests
zbMATH Keywords: spatio-temporal extremes
describes a project that uses: astsa
describes a project that uses: ElemStatLearn
MaRDI profile type: Publication
full work available at URL: s10687-018-0323-y
OpenAlex ID: W2800659164
cites work: Random forests
cites work: Extremal quantile regression
cites work: A spatio-temporal dynamic regression model for extreme wind speeds
cites work: Q3093378
cites work: Consistency of random forests
cites work: Time Series Analysis and Its Applications
Wikidata QID: Q129848634
DOI: 10.1007/S10687-018-0323-Y
description: scientific article; zbMATH DE number 6952744