Extropy and entropy estimation based on progressive Type-I interval censoring

This paper proposes nonparametric estimates for the two information measures extropy and entropy when a progressively Type-I interval censored data is available. Different nonparametric approaches are used for deriving the estimates, including: moments of the empirical cumulative distribution function and linear regression. The performance of the proposed estimates is studied under various censoring schemes via simulation studies. Furthermore, different real data sets are analyzed for illustrative purposes.The estimates based on linear approximation J^2 and H^2 outperform the other estimate in the majority of studied cases.