Abstract

In presence of covariates, dependence between random variables can be modelized using conditional copula. Whenever the copula function belongs to a given parametric family, an important question is to modelize the relationship between the copula dependence parameter and some covariate, which is described by the so-called calibration function. In this paper, we propose a wavelet regression approach to estimate this calibration function. We discuss asymptotic minimax properties of the linear and non-linear wavelets estimators and show their performance via a simulation study. An application to meteorological data reveals that the temperature influences the dependence structure between the maximum and the minimum relative humidity variables, when it takes high values.