Connections between Weighted Generalized Cumulative Residual Entropy and Variance

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 g...

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Main Authors: Abdolsaeed Toomaj, Antonio Di Crescenzo
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/8/7/1072
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spelling doaj-5a4a334ea5234a22abcf17169de67f6f2020-11-25T03:44:25ZengMDPI AGMathematics2227-73902020-07-0181072107210.3390/math8071072Connections between Weighted Generalized Cumulative Residual Entropy and VarianceAbdolsaeed Toomaj0Antonio Di Crescenzo1Department of Mathematics and Statistics, Faculty of Basic Sciences and Engineering, Gonbad Kavous University, Gonbad Kavous, IranDipartimento di Matematica, Università di Salerno, Via Giovanni Paolo II n.132, I-84084 Fisciano (SA), ItalyA 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.https://www.mdpi.com/2227-7390/8/7/1072weighted generalized cumulative residual entropynon-homogeneous Poisson processexcess wealth transformshock modelvariance
collection DOAJ
language English
format Article
sources DOAJ
author Abdolsaeed Toomaj
Antonio Di Crescenzo
spellingShingle Abdolsaeed Toomaj
Antonio Di Crescenzo
Connections between Weighted Generalized Cumulative Residual Entropy and Variance
Mathematics
weighted generalized cumulative residual entropy
non-homogeneous Poisson process
excess wealth transform
shock model
variance
author_facet Abdolsaeed Toomaj
Antonio Di Crescenzo
author_sort Abdolsaeed Toomaj
title Connections between Weighted Generalized Cumulative Residual Entropy and Variance
title_short Connections between Weighted Generalized Cumulative Residual Entropy and Variance
title_full Connections between Weighted Generalized Cumulative Residual Entropy and Variance
title_fullStr Connections between Weighted Generalized Cumulative Residual Entropy and Variance
title_full_unstemmed Connections between Weighted Generalized Cumulative Residual Entropy and Variance
title_sort connections between weighted generalized cumulative residual entropy and variance
publisher MDPI AG
series Mathematics
issn 2227-7390
publishDate 2020-07-01
description 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.
topic weighted generalized cumulative residual entropy
non-homogeneous Poisson process
excess wealth transform
shock model
variance
url https://www.mdpi.com/2227-7390/8/7/1072
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