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Connections between Weighted Generalized Cumulative Residual Entropy and Variance

Abdolsaeed Toomaj () and Antonio Di Crescenzo ()
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Abdolsaeed Toomaj: Department of Mathematics and Statistics, Faculty of Basic Sciences and Engineering, Gonbad Kavous University, Gonbad Kavous, Iran
Antonio Di Crescenzo: Dipartimento di Matematica, Università di Salerno, Via Giovanni Paolo II n.132, I-84084 Fisciano (SA), Italy

Mathematics, 2020, vol. 8, issue 7, 1-27

Abstract: A shift-dependent information measure is favorable to handle in some specific applied contexts such as mathematical neurobiology and survival analysis. For this reason, the weighted differential entropy has been introduced in the literature. In accordance with this measure, we propose the weighted generalized cumulative residual entropy as well. Despite existing apparent similarities between these measures, however, there are quite substantial and subtle differences between them because of their different metrics. In this paper, particularly, we show that the proposed measure is equivalent to the generalized cumulative residual entropy of the cumulative weighted random variable. Thus, we first provide expressions for the variance and the new measure in terms of the weighted mean residual life function and then elaborate on some characteristics of such measures, including equivalent expressions, stochastic comparisons, bounds, and connection with the excess wealth transform. Finally, we also illustrate some applications of interest in system reliability with reference to shock models and random minima.

Keywords: weighted generalized cumulative residual entropy; non-homogeneous Poisson process; excess wealth transform; shock model; variance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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