Application of Entropy to a Life-Time Model

Awad, Adnan M.; Alawneh, Ameen

doi:10.1093/imamci/4.2.143

Cited by 25 publications

(8 citation statements)

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“…Among all, we mention the cumulative residual entropy [12], the average entropy [13] and in particular, the sup-entropy [14]. The sup-entropy has been recently used as a quantifier of the efficiency of the censored sample for several survival distributions such as exponential distribution [15, 16] , Pareto [17] , Weibull [18, 19]. However, none of these works applied the measure on real data nor provided a clear physical meaning of this important quantity.…”

Section: Introductionmentioning

confidence: 99%

Comparison of two-lifetime models of solid-state lighting based on sup-entropy

Kittaneh

Majid

2019

Heliyon

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Section: Introductionmentioning

confidence: 99%

Comparison of two-lifetime models of solid-state lighting based on sup-entropy

Kittaneh

Majid

2019

Heliyon

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“…For all other schemes in CS (3,8) that are not mentioned above, the above inequalities in (a) of scheme * + hold. g) When is close enough to the minimum scheme when is * + while it is * + when On the other hand, the maximum scheme is * + when , while it is * + when .…”

Section: Numerical Computationsmentioning

confidence: 94%

On Optimal Designs of Some Censoring Schemes

Awad

2016

Pak.j.stat.oper.res.

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The main objective of this paper is to explore suitability of some entropy-information measures for introducing a new optimality censoring criterion and to apply it to some censoring schemes from some underlying life-time models. In addition, the paper investigates four related issues namely; the effect of the parameter of parent distribution on optimal scheme, equivalence of schemes based on Shannon and Awad sup-entropy measures, the conjecture that the optimal scheme is one stage scheme, and a conjecture by Cramer and Bagh (2011) about Shannon minimum and maximum schemes when parent distribution is reflected power. Guidelines for designing an optimal censoring plane are reported together with theoretical and numerical results and illustrations.

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“…For the two entropy measures proposed by [27], it is supposed that sup x2R f ðx; φÞ is finite and well identified.…”

Section: Various Entropy Measuresmentioning

confidence: 99%

Estimation of different types of entropies for the Kumaraswamy distribution

et al. 2021

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The estimation of the entropy of a random system or process is of interest in many scientific applications. The aim of this article is the analysis of the entropy of the famous Kumaraswamy distribution, an aspect which has not been the subject of particular attention previously as surprising as it may seem. With this in mind, six different entropy measures are considered and expressed analytically via the beta function. A numerical study is performed to discuss the behavior of these measures. Subsequently, we investigate their estimation through a semi-parametric approach combining the obtained expressions and the maximum likelihood estimation approach. Maximum likelihood estimates for the considered entropy measures are thus derived. The convergence properties of these estimates are proved through a simulated data, showing their numerical efficiency. Concrete applications to two real data sets are provided.

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Application of Entropy to a Life-Time Model

Cited by 25 publications

References 1 publication

Comparison of two-lifetime models of solid-state lighting based on sup-entropy

Comparison of two-lifetime models of solid-state lighting based on sup-entropy

On Optimal Designs of Some Censoring Schemes

Estimation of different types of entropies for the Kumaraswamy distribution

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