Empirical likelihood confidence regions for one- or two-samples with doubly censored data

The purpose is to propose a new EM algorithm for doubly censored data subject to non-parametric moment constraints. Empirical likelihood confidence regions are constructed for one- or two-samples of doubly censored data. It is shown that the corresponding empirical likelihood ratio converges to a standard chi-square random variable. Simulations are carried out to assess the finite-sample performance of the proposed method. For illustration purpose, the proposed method is applied to a real data set with two samples. (C) 2015 Elsevier B.V. All rights reserved.