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Biometrika 2003 90(1):139-156; doi:10.1093/biomet/90.1.139
© 2003 by Biometrika Trust
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A dependence measure for multivariate and spatial extreme values: Properties and inference

Martin Schlather1 and Jonathan A.Tawn2

1 Soil Physics Group, University of Bayreuth, 95440 Bayreuth, Germany martin.schlather{at}uni-bayreuth.de 2 Department of Mathematics and Statistics, Lancaster University, Lancaster LA1 4YF, U.Kj.tawn{at}lancaster.ac.uk

We present properties of a dependence measure that arises in the study of extreme values in multivariate and spatial problems. For multivariate problems the dependence measure characterises dependence at the bivariate level, for all pairs and all higher orders up to and including the dimension of the variable.Necessary and sufficient conditions are given for subsets of dependence measures to be self-consistent, that is to guarantee the existence of a distribution with such a subset of values for the dependence measure. For pairwise dependence, these conditions are given in terms of positive semidefinite matrices and non-differentiable, positive definite functions. We construct new nonparametric estimators for the dependence measure which, unlike all naive nonparametric estimators, impose these self-consistency properties. As the new estimators provide an improvement on the naive methods, both in terms of the inferential and interpretability properties, their use in exploratory extreme value analyses should aid the identification of appropriate dependence models. The methods are illustrated through an analysis of simulated multivariate data, which shows that a lack of self-consistency is frequently a problem with the existing estimators, and by a spatial analysis of daily rainfall extremes in south-west England, which finds a smooth decay in extremal dependence with distance.

Key Words: Dependence measure; Extreme value; Max-stable process; Multivariate extreme-value distribution

Received August 2001. Revised April 2002


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