Bivariate censored regression relying on a new estimator of the joint distribution function

In this paper we study a class of M-estimators in a regression model under bivariate random censoring and provide a set of sufficient conditions that ensure asymptotic n ^1/2-convergence. The cornerstone of our approach is a new estimator of the joint distribution function of the censored lifetimes. A copula approach is used to modelize the dependence structure between the bivariate censoring times. The resulting estimators present the advantage of being easily computable. A simulation study enlighten the finite sample behavior of this technique.