64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

A wavelet regression approach for dependence calibration in conditional copula model


Cheikh Tidiane Seck


  • A
    Aba Diop


64th ISI World Statistics Congress - Ottawa, Canada

Format: CPS Paper

Session: CPS 86 - Statistical modelling V

Thursday 20 July 8:30 a.m. - 9:40 a.m. (Canada/Eastern)


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.