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

Huda H.   Qubbaj; Husam A. Bayoud; Hisham M. Hilow

doi:https://doi.org/10.59170/stattrans-2024-029

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

Huda H. Qubbaj Department of Mathematics, The University of Jordan, Jordan ORCID:https://orcid.org/0009-0004-9364-5082 , Husam A. Bayoud College of Sciences and Humanities, Fahad Bin Sultan University, Saudi Arabia ORCID: https://orcid.org/0000-0002-0066-7206 , Hisham M. Hilow Department of Mathematics, The University of Jordan, Jordan ORCID:https://orcid.org/0000-0001-7291-3579 Statistics in Transition new series, vol. 25, 2024, 3, pages: 83-102 Published online: 4 September 2024 https://doi.org/10.59170/stattrans-2024-029 Citation: Qubbaj H. H., Bayoud H. A., Hilow H. M., 2024. Extropy and entropy estimation based on progressive Type-I interval censoring. Statistics in Transition new series, 25(3), pp. 83-102. https://doi.org/10.59170/stattrans-2024-029

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ARTICLE

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ABSTRACT

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.

KEYWORDS

entropy; extropy; mean square error; nonparametric statistics; Monte Carlo simulation; Type-I interval censoring

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