A0654
Title: Residual-based estimation of parametric copulas under regression
Authors: Yue Zhao - University of York (United Kingdom) [presenting]
Abstract: A multivariate response regression model is studied where each coordinate is described by a location-scale regression, and where the dependence structure of the ``noise'' terms in the regression is described by a parametric copula. The goal is to estimate the associated Euclidean copula parameter given a sample of the response and the covariate. In the absence of the copula sample, the oracle ranks in the usual pseudo-likelihood estimation procedure are no longer computable. Instead, we base our estimation on the residual ranks calculated from some preliminary estimators of the regression functions. We show that the residual-based estimators are asymptotically equivalent to their oracle counterparts, even under severe divergence of the criterion functions in pseudo-likelihood estimation and when the dimension of the covariate in the regression is moderately diverging. Partially to serve this objective, we also study the weighted convergence of the residual empirical processes.
