Bootstrapping the conditional copula

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10139488" target="_blank" >RIV/00216208:11320/13:10139488 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.jspi.2012.06.001" target="_blank" >http://dx.doi.org/10.1016/j.jspi.2012.06.001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jspi.2012.06.001" target="_blank" >10.1016/j.jspi.2012.06.001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bootstrapping the conditional copula

Popis výsledku v původním jazyce

This paper is concerned with inference about the dependence or association between two random variables conditionally upon the given value of a covariate. A way to describe such a conditional dependence is via a conditional copula function. Nonparametricestimators for a conditional copula then lead to nonparametric estimates of conditional association measures such as a conditional Kendall's tau. The limiting distributions of nonparametric conditional copula estimators are rather involved. In this paper we propose a bootstrap procedure for approximating these distributions and their characteristics, and establish its consistency. We apply the proposed bootstrap procedure for constructing confi dence intervals for conditional association measures, suchas a conditional Blomqvist beta and a conditional Kendall's tau. The performances of the proposed methods are investigated via a simulation study involving a variety of models, ranging from models in which the dependence (weak or strong)

Název v anglickém jazyce

Bootstrapping the conditional copula

Popis výsledku anglicky

This paper is concerned with inference about the dependence or association between two random variables conditionally upon the given value of a covariate. A way to describe such a conditional dependence is via a conditional copula function. Nonparametricestimators for a conditional copula then lead to nonparametric estimates of conditional association measures such as a conditional Kendall's tau. The limiting distributions of nonparametric conditional copula estimators are rather involved. In this paper we propose a bootstrap procedure for approximating these distributions and their characteristics, and establish its consistency. We apply the proposed bootstrap procedure for constructing confi dence intervals for conditional association measures, suchas a conditional Blomqvist beta and a conditional Kendall's tau. The performances of the proposed methods are investigated via a simulation study involving a variety of models, ranging from models in which the dependence (weak or strong)

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/GPP201%2F11%2FP290" target="_blank" >GPP201/11/P290: Metody statistické inference založené na matici párových vzdáleností</a><br>

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Journal of Statistical Planning and Inference

ISSN

0378-3758

e-ISSN

—

Svazek periodika

143

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

23

Strana od-do

1-23

Kód UT WoS článku

000309845700001

EID výsledku v databázi Scopus

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