Conditional copulas, association measures and their applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F11%3A10100436" target="_blank" >RIV/00216208:11320/11:10100436 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.csda.2010.11.010" target="_blank" >http://dx.doi.org/10.1016/j.csda.2010.11.010</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csda.2010.11.010" target="_blank" >10.1016/j.csda.2010.11.010</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Conditional copulas, association measures and their applications

Popis výsledku v původním jazyce

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. Thennonparametric estimates for the conditional association measures are derived. A key issue is that these measures are now looked at as functions in the covariate.

Název v anglickém jazyce

Conditional copulas, association measures and their applications

Popis výsledku anglicky

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. Thennonparametric estimates for the conditional association measures are derived. A key issue is that these measures are now looked at as functions in the covariate.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

<a href="/cs/project/LC06024" target="_blank" >LC06024: Centrum Jaroslava Hájka pro teoretickou a aplikovanou statistiku</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computational Statistics and Data Analysis

ISSN

0167-9473

e-ISSN

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Svazek periodika

55

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

1919-1932

Kód UT WoS článku

000287952900003

EID výsledku v databázi Scopus

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