Tail-weighted dependence measures with limit being the tail dependence coefficient
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AbstractFor bivariate continuous data, measures of monotonic dependence are based on the rank transformations of the two variables. For bivariate extreme value copulas, there is a family of estimators (Formula presented.), for (Formula presented.), of the extremal coefficient, based on a transform of the absolute difference of the α power of the ranks. In the case of general bivariate copulas, we obtain the probability limit (Formula presented.) of (Formula presented.) as the sample size goes to infinity and show that (i) (Formula presented.) for (Formula presented.) is a measure of central dependence with properties similar to Kendall's tau and Spearman's rank correlation, (ii) (Formula presented.) is a tail-weighted dependence measure for large α, and (iii) the limit as (Formula presented.) is the upper tail dependence coefficient. We obtain asymptotic properties for the rank-based measure (Formula presented.) and estimate tail dependence coefficients through extrapolation on (Formula presented.). A data example illustrates the use of the new dependence measures for tail inference.
CitationLee D, Joe H, Krupskii P (2017) Tail-weighted dependence measures with limit being the tail dependence coefficient. Journal of Nonparametric Statistics: 1–29. Available: http://dx.doi.org/10.1080/10485252.2017.1407414.
SponsorsThe authors would like to thank the anonymous referees for their useful comments and suggestions.
PublisherInforma UK Limited