文章基本信息

标题：Multivariate and functional covariates and conditional copulas
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作者：Irène Gijbels ; Marek Omelka ; Noël Veraverbeke 等
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2012
卷号：6
页码：1273-1306
DOI：10.1214/12-EJS712
语种：English
出版社：Institute of Mathematical Statistics
摘要：In this paper the interest is to estimate the dependence between two variables conditionally upon a covariate, through copula modelling. In recent literature nonparametric estimators for conditional copula functions in case of a univariate covariate have been proposed. The aim of this paper is to nonparametrically estimate a conditional copula when the covariate takes on values in more complex spaces. We consider multivariate covariates and functional covariates. We establish weak convergence, and bias and variance properties of the proposed nonparametric estimators. We also briefly discuss nonparametric estimation of conditional association measures such as a conditional Kendall’s tau. The case of functional covariates is of particular interest and challenge, both from theoretical as well as practical point of view. For this setting we provide an illustration with a real data example in which the covariates are spectral curves. A simulation study investigating the finite-sample performances of the discussed estimators is provided.