Estimation of entropy and extropy based on right censored data: A Bayesian non-parametric approach

Al-Labadi Luai () and Tahir Muhammad ()
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Al-Labadi Luai: Department of Mathematical and Computational Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada
Tahir Muhammad: Department of Mathematical and Computational Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada

Monte Carlo Methods and Applications, 2022, vol. 28, issue 4, 319-328

Abstract: Entropy and extropy are central measures in information theory. In this paper, Bayesian non-parametric estimators to entropy and extropy with possibly right censored data are proposed. The approach uses the beta-Stacy process and the difference operator. Examples are presented to illustrate the performance of the estimators.

Keywords: Beta-Stacy process; entropy; extropy; non-parametric Bayesian statistics; right censored data (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/mcma-2022-2123

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