Space-time modelling of extreme events
Space-time modelling of extreme events
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DOI：
10.1111/rssb.12035
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发表时间：
2014-03-01
影响因子：
5.8
通讯作者：
Davison, A. C.
中科院分区：
文献类型：
--
作者：
Huser, R.;Davison, A. C.
最大稳定过程是在模拟空间和时间上的极端事件时广义极值分布的自然类似物。在适当的条件下，这些过程是渐近合理的模型的最大值的独立复制的随机场，他们也适合于建模的极端测量超过高阈值。本文展示了如何成对删失的可能性可以用于一致估计的极端的空间-时间数据在温和的混合条件下，并说明了这一点，通过拟合扩展的模型，由于Schlather每小时的降雨量数据。块引导程序用于不确定度评估。估计效率被认为是成对似然的选择被包括在讨论。所提出的模型比一些自然竞争对手更好地拟合数据。
Max-stable processes are the natural analogues of the generalized extreme value distribution when modelling extreme events in space and time. Under suitable conditions, these processes are asymptotically justified models for maxima of independent replications of random fields, and they are also suitable for the modelling of extreme measurements over high thresholds. The paper shows how a pairwise censored likelihood can be used for consistent estimation of the extremes of space–time data under mild mixing conditions and illustrates this by fitting an extension of a model due to Schlather to hourly rainfall data. A block bootstrap procedure is used for uncertainty assessment. Estimator efficiency is considered and the choice of pairs to be included in the pairwise likelihood is discussed. The model proposed fits the data better than some natural competitors.