Estimation of a Conditional Copula and Association Measures

被引：68

作者：

Veraverbeke, Noel ^{[3
]}

Omelka, Marek ^{[4
]}

Gijbels, Irene ^{[1
,2
]}

机构：

[1] Katholieke Univ Leuven, Dept Math, B-3001 Heverlee, Belgium

[2] Katholieke Univ Leuven, Leuven Stat Res Ctr LStat, B-3001 Heverlee, Belgium

[3] Hasselt Univ, Ctr Stat, Diepenbeek, Belgium

[4] Charles Univ Prague, Dept Probabil & Stat, Fac Math & Phys, Prague, Czech Republic

来源：

SCANDINAVIAN JOURNAL OF STATISTICS | 2011年 / 38卷 / 04期

关键词：

asymptotic representation; conditional Kendall's tau; empirical copula process; fixed design; random design; smoothing; weak convergence;

D O I：

10.1111/j.1467-9469.2011.00744.x

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper is concerned with studying the dependence structure between two random variables Y(1) and Y(2) conditionally upon a covariate X. The dependence structure is modelled via a copula function, which depends on the given value of the covariate in a general way. Gijbels et. al. (Comput. Statist. Data Anal., 55, 2011, 1919) suggested two non-parametric estimators of the conditional copula and investigated their numerical performances. In this paper we establish the asymptotic properties of the proposed estimators as well as conditional association measures derived from them. Practical recommendations for their use are then discussed.

引用

页码：766 / 780

页数：15

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