An algorithm for maximizing Kendalls tau

For the censored regression model, Yang (J. Amer. Statist. Assoc. 92 (1997) 977¯984) introduced a new class of estimating functions. These estimating functions produce regression estimators that are asymptotically normal with a density-free asymptotic variance that is simple to estimate reliably. In this paper we further study the estimation function of Yang by considering new classes of weights. Through extensive numerical studies, we find weights that enhance the results of the estimating function and improve upon choices previously recommended by Yang.