Information bounds and efficient estimation in a class of censored transformation models

Abstract

Transformation models provide a common tool for regression analysis of censored failure time data. The most common approach towards parameter estimation in these models is based on the nonparametric profile likelihood method. Several authors proposed also ad hoc M-estimators of the Euclidean component of the model. These estimators are usually simpler to impelement and many of them have good practical performance. In this paper we consider the form of the information bound for estimation if the Euclidean parameter of the model and propose a modification of inefficient M-estimators to one-step maximum likelihood estimates.