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Detecting a conditional extreme value model

Content Provider	Springer Nature Link
Author	Das, Bikramjit Resnick, Sidney I.
Copyright Year	2009
Abstract	In classical extreme value theory probabilities of extreme events are estimated assuming all the components of a random vector to be in a domain of attraction of an extreme value distribution. In contrast, the conditional extreme value model assumes a domain of attraction condition on a sub-collection of the components of a multivariate random vector. This model has been studied in Heffernan and Tawn (JRSS B 66(3):497–546, 2004), Heffernan and Resnick (Ann Appl Probab 17(2):537–571, 2007), and Das and Resnick (2009). In this paper we propose three statistics which act as tools to detect this model in a bivariate set-up. In addition, the proposed statistics also help to distinguish between two forms of the limit measure that is obtained in the model.
Starting Page	29
Ending Page	61
Page Count	33
File Format	PDF
ISSN	13861999
Journal	Extremes
Volume Number	14
Issue Number	1
e-ISSN	1572915X
Language	English
Publisher	Springer US
Publisher Date	2009-12-10
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Regular variation Domain of attraction Heavy tails Asymptotic independence Conditional extreme value model Quality Control, Reliability, Safety and Risk Environmental Management Civil Engineering Statistics Hydrogeology Statistics for Business/Economics/Mathematical Finance/Insurance
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Economics, Econometrics and Finance Engineering

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