Consistent estimation of survival functions under uniform stochastic ordering; the k-sample case

Abstract

Let S-1, S-2, ... , S-k be survival functions of life distributions. They are said to be uniformly stochastically ordered, S-1 <=(uso) S-2 <=(uso) ... <=(uso) S-k, if S-i/Si+1 is a survival function for 1 <= i <= k - 1. The nonparametric maximum likelihood estimators of the survival functions subject to this ordering constraint are known to be inconsistent in general. Consistent estimators were developed only for the case of k = 2. In this paper we provide consistent estimators in the k-sample case, with and without censoring. In proving consistency, we needed to develop a new algorithm for isotonic regression that may be of independent interest.