基于最稳定过程的极端风暴区域模拟

S. Coles
{"title":"基于最稳定过程的极端风暴区域模拟","authors":"S. Coles","doi":"10.1111/J.2517-6161.1993.TB01941.X","DOIUrl":null,"url":null,"abstract":"Asymptotic models for extremes of random processes often form the basis for estimating the extremal behaviour of environmental phenomena. Most such phenomena have a spatial dimension, and the aim of this paper is to develop a procedure for modelling in continuous space the spatial dependence within extreme events. A principal objective in the analysis-as with other current research on extremes-is to base inference on as much of the available data as possible. The modelling procedures are justified on simulated data and subsequently applied to a series of rainfall data","PeriodicalId":17425,"journal":{"name":"Journal of the royal statistical society series b-methodological","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1993-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"118","resultStr":"{\"title\":\"Regional Modelling of Extreme Storms Via Max‐Stable Processes\",\"authors\":\"S. Coles\",\"doi\":\"10.1111/J.2517-6161.1993.TB01941.X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Asymptotic models for extremes of random processes often form the basis for estimating the extremal behaviour of environmental phenomena. Most such phenomena have a spatial dimension, and the aim of this paper is to develop a procedure for modelling in continuous space the spatial dependence within extreme events. A principal objective in the analysis-as with other current research on extremes-is to base inference on as much of the available data as possible. The modelling procedures are justified on simulated data and subsequently applied to a series of rainfall data\",\"PeriodicalId\":17425,\"journal\":{\"name\":\"Journal of the royal statistical society series b-methodological\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"118\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the royal statistical society series b-methodological\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/J.2517-6161.1993.TB01941.X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the royal statistical society series b-methodological","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/J.2517-6161.1993.TB01941.X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 118

摘要

随机过程极值的渐近模型通常是估计环境现象极值行为的基础。大多数这样的现象都有空间维度,本文的目的是开发一个在连续空间中模拟极端事件的空间依赖性的程序。分析的一个主要目标——与当前其他关于极端现象的研究一样——是根据尽可能多的可用数据进行推断。模拟数据证明了模型程序的合理性,并随后应用于一系列降雨数据
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Regional Modelling of Extreme Storms Via Max‐Stable Processes
Asymptotic models for extremes of random processes often form the basis for estimating the extremal behaviour of environmental phenomena. Most such phenomena have a spatial dimension, and the aim of this paper is to develop a procedure for modelling in continuous space the spatial dependence within extreme events. A principal objective in the analysis-as with other current research on extremes-is to base inference on as much of the available data as possible. The modelling procedures are justified on simulated data and subsequently applied to a series of rainfall data
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Proposal of the vote of thanks in discussion of Cule, M., Samworth, R., and Stewart, M.: Maximum likelihood estimation of a multidimensional logconcave density On Assessing goodness of fit of generalized linear models to sparse data Bayes Linear Sufficiency and Systems of Expert Posterior Assessments On the Choice of Smoothing Parameter, Threshold and Truncation in Nonparametric Regression by Non-linear Wavelet Methods Quasi‐Likelihood and Generalizing the Em Algorithm
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1