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Article: Tail-weighted dependence measures with limit being the tail dependence coefficient
Title | Tail-weighted dependence measures with limit being the tail dependence coefficient |
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Authors | |
Keywords | Copula extremal coefficient monotone dependence tail order tail-weighted dependence |
Issue Date | 2018 |
Publisher | Taylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10485252.asp |
Citation | Journal of Nonparametric Statistics, 2018, v. 30 n. 2, p. 262-290 How to Cite? |
Abstract | For 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 ${hatvartheta}_alpha$, for $alpha>0$, of the extremal coefficient, based on a transform of the absolute difference of the $alpha$ power of the ranks. In the case of general bivariate copulas, we obtain the probability limit $zeta_alpha$ of $hat{zeta}_alpha=2-{hatvartheta}_alpha$ as the sample size goes to infinity, and show that (i) $zeta_alpha$ for $alpha=1$ is a measure of central dependence with properties similar to Kendall's tau and Spearman's rank correlation, (ii) $zeta_alpha$ is a tail-weighted dependence measure for large $alpha$, and (iii) the limit as $alpha oinfty$ is the upper tail dependence coefficient. We obtain asymptotic properties for the rank-based measure ${hatzeta}_alpha$, and estimate tail dependence coefficients through extrapolation on ${hatzeta}_alpha$. A data example illustrates the use of the new dependence measures for tail inference. |
Persistent Identifier | http://hdl.handle.net/10722/259500 |
ISSN | 2021 Impact Factor: 1.012 2020 SCImago Journal Rankings: 0.735 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lee, D | - |
dc.contributor.author | Joe, H | - |
dc.contributor.author | Krupskii, P | - |
dc.date.accessioned | 2018-09-03T04:08:50Z | - |
dc.date.available | 2018-09-03T04:08:50Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Journal of Nonparametric Statistics, 2018, v. 30 n. 2, p. 262-290 | - |
dc.identifier.issn | 1048-5252 | - |
dc.identifier.uri | http://hdl.handle.net/10722/259500 | - |
dc.description.abstract | For 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 ${hatvartheta}_alpha$, for $alpha>0$, of the extremal coefficient, based on a transform of the absolute difference of the $alpha$ power of the ranks. In the case of general bivariate copulas, we obtain the probability limit $zeta_alpha$ of $hat{zeta}_alpha=2-{hatvartheta}_alpha$ as the sample size goes to infinity, and show that (i) $zeta_alpha$ for $alpha=1$ is a measure of central dependence with properties similar to Kendall's tau and Spearman's rank correlation, (ii) $zeta_alpha$ is a tail-weighted dependence measure for large $alpha$, and (iii) the limit as $alpha oinfty$ is the upper tail dependence coefficient. We obtain asymptotic properties for the rank-based measure ${hatzeta}_alpha$, and estimate tail dependence coefficients through extrapolation on ${hatzeta}_alpha$. A data example illustrates the use of the new dependence measures for tail inference. | - |
dc.language | eng | - |
dc.publisher | Taylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10485252.asp | - |
dc.relation.ispartof | Journal of Nonparametric Statistics | - |
dc.rights | This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Nonparametric Statistics on 02 Dec 2017, available online: http://www.tandfonline.com/10.1080/10485252.2017.1407414 | - |
dc.subject | Copula | - |
dc.subject | extremal coefficient | - |
dc.subject | monotone dependence | - |
dc.subject | tail order | - |
dc.subject | tail-weighted dependence | - |
dc.title | Tail-weighted dependence measures with limit being the tail dependence coefficient | - |
dc.type | Article | - |
dc.identifier.email | Lee, D: leedav@hku.hk | - |
dc.identifier.authority | Lee, D=rp02276 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1080/10485252.2017.1407414 | - |
dc.identifier.scopus | eid_2-s2.0-85035799764 | - |
dc.identifier.hkuros | 288844 | - |
dc.identifier.volume | 30 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 262 | - |
dc.identifier.epage | 290 | - |
dc.identifier.isi | WOS:000437353300001 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 1026-7654 | - |