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http://hdl.handle.net/1942/11827
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DC Field | Value | Language |
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dc.contributor.author | Gijbels, Irene | - |
dc.contributor.author | VERAVERBEKE, Noel | - |
dc.contributor.author | OMELKA, Marek | - |
dc.date.accessioned | 2011-03-23T07:24:25Z | - |
dc.date.available | NO_RESTRICTION | - |
dc.date.available | 2011-03-23T07:24:25Z | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | COMPUTATIONAL STATISTICS & DATA ANALYSIS, 55 (5). p. 1919-1932 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | http://hdl.handle.net/1942/11827 | - |
dc.description.abstract | One way to model a dependence structure is through the copula function which is a mean to capture the dependence structure in the joint distribution of variables. Association measures such as Kendall's tau or Spearman's rho can be expressed as functionals of the copula. The dependence structure between two variables can be highly influenced by a covariate, and it is of real interest to know how this dependence structure changes with the value taken by the covariate. This motivates the need for introducing conditional copulas, and the associated conditional Kendall's tau and Spearman's rho association measures. After the introduction and motivation of these concepts, two nonparametric estimators for a conditional copula are proposed and discussed. Then nonparametric estimates for the conditional association measures are derived. A key issue is that these measures are now looked at as functions in the covariate. The performances of all estimators are investigated via a simulation study which also includes a data-driven algorithm for choosing the smoothing parameters. The usefulness of the methods is illustrated on two real data examples. (C) 2010 Elsevier B.V. All rights reserved. | - |
dc.description.sponsorship | The authors would like to thank the Associate Editor and two reviewers for their valuable remarks which led to the inclusion of Section 5. This work was supported by the IAP Research Network P6/03 of the Belgian State (Belgian Science Policy). This work was started while Marek Omelka was a postdoctoral researcher at the Katholieke Universiteit Leuven and the Universiteit Hasselt within the IAP Research Network. The support of Project LC06024 is also highly appreciated. The first author gratefully acknowledges support from the GOA/07/04-project of the Research Fund K.U.Leuven. The second author acknowledges support from Research Grant MTM 2008-03129 of the Spanish Ministerio de Ciencia e Innovacion. | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject.other | Asymptotic bias; Asymptotic variance; Conditional copula; Conditional Kendall's tau; Conditional Spearman's rho; Empirical estimation; Global and local bandwidths; Local dependencies; Smoothing | - |
dc.subject.other | Asymptotic bias; Asymptotic variance; Conditional copula; Conditional Kendall's tau; Conditional Spearman's rho; Empirical estimation; Global and local bandwidths; Local dependencies; Smoothing | - |
dc.title | Conditional copulas, association measures and their applications | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 1932 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 1919 | - |
dc.identifier.volume | 55 | - |
local.format.pages | 14 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | [Gijbels, Irene] Katholieke Univ Leuven, Dept Math, B-3001 Heverlee, Belgium. [Gijbels, Irene] Katholieke Univ Leuven, Leuven Stat Res Ctr LStat, B-3001 Heverlee, Belgium. [Veraverbeke, Noel] Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium. [Omelka, Marel] Charles Univ Prague, Jaroslav Hajek Ctr Theoret & Appl Stat, Prague 18675 8, Czech Republic. Irene.Gijbels@wis.kuleuven.be; noel.veraverbeke@uhasselt.be; omelka@karlin.mff.cuni.cz | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1016/j.csda.2010.11.010 | - |
dc.identifier.isi | 000287952900003 | - |
item.fulltext | With Fulltext | - |
item.contributor | Gijbels, Irene | - |
item.contributor | VERAVERBEKE, Noel | - |
item.contributor | OMELKA, Marek | - |
item.validation | ecoom 2012 | - |
item.fullcitation | Gijbels, Irene; VERAVERBEKE, Noel & OMELKA, Marek (2011) Conditional copulas, association measures and their applications. In: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 55 (5). p. 1919-1932. | - |
item.accessRights | Restricted Access | - |
crisitem.journal.issn | 0167-9473 | - |
crisitem.journal.eissn | 1872-7352 | - |
Appears in Collections: | Research publications |
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veraverbeke.pdf Restricted Access | Published version | 972.93 kB | Adobe PDF | View/Open Request a copy |
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