A Large Sample Study of Rank Estimation for Censored Regression Data

Abstract

Large sample approximations are developed to establish asymptotic linearity of the commonly used linear rank estimating functions, defined as stochastic integrals of counting processes over the whole line, for censored regression data. These approximations lead to asymptotic normality of the resulting rank estimators defined as solutions of the linear rank estimating equations. A second kind of approximations is also developed to show that the estimating functions can be uniformly approximated by certain more manageable nonrandom functions, resulting in a simple condition that guarantees consistency of the rank estimators. This condition is verified for the two-sample problem, thereby extending earlier results by Louis and Wei and Gail, as well as in the case when the underlying error distribution has increasing failure rate, which includes most parametric regression models in survival analysis. Techniques to handle the delicate tail fluctuations are provided and discussed in detail.