@article { ISI:000327908200006,
	title = {Geometric interpretation of the residual dependence coefficient},
	journal = {JOURNAL OF MULTIVARIATE ANALYSIS},
	volume = {123},
	year = {2014},
	month = {JAN},
	pages = {85-95},
	publisher = {ELSEVIER INC},
	type = {Article},
	address = {525 B STREET, STE 1900, SAN DIEGO, CA 92101-4495 USA},
	abstract = {The residual dependence coefficient was originally introduced by Ledford and Tawn (1996) [25] as a measure of residual dependence between extreme values in the presence of asymptotic independence. We present a geometric interpretation of this coefficient with the additional assumptions that the random samples from a given distribution can be scaled to converge onto a limit set and that the marginal distributions have Weibull-type tails. This result leads to simple and intuitive computations of the residual dependence coefficient for a variety of distributions. (C) 2013 Elsevier Inc. All rights reserved.},
	keywords = {Asymptotic independence, Geometric approach, Limit set, Multivariate density, Residual dependence coefficient, Sample clouds},
	issn = {0047-259X},
	doi = {10.1016/j.jmva.2013.08.018},
	author = {Nolde, Natalia}
}
@article { ISI:000306097600010,
	title = {ASYMPTOTIC DEPENDENCE FOR LIGHT-TAILED HOMOTHETIC DENSITIES},
	journal = {ADVANCES IN APPLIED PROBABILITY},
	volume = {44},
	number = {2},
	year = {2012},
	month = {JUN},
	pages = {506-527},
	publisher = {APPLIED PROBABILITY TRUST},
	type = {Article},
	address = {THE UNIVERSITY, SCHOOL MATHEMATICS STATISTICS, SHEFFIELD S3 7RH, ENGLAND},
	abstract = {Dependence between coordinate extremes is a key factor in any multivariate risk assessment. Hence, it is of interest to know whether the components of a given multivariate random vector exhibit asymptotic independence or asymptotic dependence. In the latter case the structure of the asymptotic dependence has to be clarified. In the multivariate setting it is common to have an explicit form of the density rather than the distribution function. In this paper we therefore give criteria for asymptotic dependence in terms of the density. We consider distributions with light tails and restrict attention to continuous unimodal densities defined on the whole space or on an open convex cone. For simplicity, the density is assumed to be homothetic: all level sets have the same shape. Balkema and Nolde (2010) contains conditions on the shape which guarantee asymptotic independence. The situation for asymptotic dependence, treated in the present paper, is more delicate.},
	keywords = {Asymptotic dependence, Geometric approach, homothetic level set, light-tailed density, multivariate extreme},
	issn = {0001-8678},
	author = {Balkema, Guus and Nolde, Natalia}
}
