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• W2043955310 abstract "Abstract Extremal dependence analysis assesses the tendency of large values of components of a random vector to occur simultaneously. This kind of dependence information can be qualitatively different than what is given by correlation which averages over the total body of the joint distribution. Also, correlation may be completely inappropriate for heavy tailed data. We study the extremal dependence measure (EDM), a measure of the tendency of large values of components of a random vector to occur simultaneously and show consistency of an estimator of the EDM. We also show asymptotic normality of an idealized estimator in a restricted case of multivariate regular variation where scaling functions do not have to be estimated." @default.
• W2043955310 created "2016-06-24" @default.
• W2043955310 creator A5003294202 @default.
• W2043955310 date "2004-12-31" @default.
• W2043955310 modified "2023-09-23" @default.
• W2043955310 title "The Extremal Dependence Measure and Asymptotic Independence" @default.
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• W2043955310 doi "https://doi.org/10.1081/stm-120034129" @default.
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