EFFICIENT ESTIMATION IN A NONLINEAR COUNTING-PROCESS REGRESSION-MODEL

Suppose we observe i.i.d. copies of X,C,Y, where X is a counting process, C is a censoring process taking only values 0 and 1, and Y is a covariate process. Assume that the intensity process of X is of the form C(s)a(s,Y(s)) with a unknown, but that the distribution of X,C,Y is unspecified otherwise. McKeague and Utikal proposed an estimator for the doubly cumulative hazard integral-0/z integral-0/t a(s,y) ds dy and determined its asymptotic distribution. We show that the estimator is regular and efficient in the sense of a Hajek-Inagaki convolution theorem for partially specified models.