Non-parametric estimation of Kullback-Leibler discrimination information based on censored data

Kullback-Leibler discrimination information is the most well-known theoretic divergence measure between two probability distributions associated with the same experiment, which finds application in the field of information theory In the present paper, we propose non-parametric estimators for the Kullback-Leibler discrimination information for the lifetime distribution based on censored data. Asymptotic properties of the estimators are established under suitable regularity conditions. Monte-Carlo simulation studies are carried out to compare the performance of the estimators based on the mean-squared error. The method is illustrated using a real data set. (C) 2019 Elsevier B.V. All rights reserved.