The residual entropy function introduced by Ebrahimi [Sankhyā A 58 (1996) 48–56], is viewed as a dynamic measure of uncertainty. This measure finds applications in modeling and analysis of life time data. In the present work, we propose nonparametric estimators for the residual entropy function based on censored data. Asymptotic properties of the estimator are established under suitable regularity conditions. Monte Carlo simulation studies are carried out to compare the performance of the estimators using the mean-squared error. The methods are illustrated using two real data sets.
"Nonparametric estimation of the residual entropy function with censored dependent data." Braz. J. Probab. Stat. 29 (4) 866 - 877, November 2015. https://doi.org/10.1214/14-BJPS250