On concomitants of ordered random variables under general forms of Morgenstern family

Said, Mohamed Mohamed

doi:10.2298/fil1909771m

Cited by 6 publications

(5 citation statements)

References 14 publications

(23 reference statements)

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“…Moreover, we concluded that the suggested estimators are influenced by sample size and almost give the same behavior in the simulation procedure. In future work, we can extend the obtained models for ordered variables and their concomitants, for more details see [19,20].…”

Section: Discussionmentioning

confidence: 99%

Some properties of cumulative extropy and its dynamic past version

Mohamed

2022

Filomat

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Extropy has been discussed in many works of literature as a complementary dual of Shannon?s entropy function. In this paper, a replacement procedure of uncertainty of random variable, constructed on the cumulative distribution function F, called cumulative extropy is proposed. Some properties and features of the deemed measure are obtained. Moreover, the dynamic form of cumulative extropy is considered. Finally, non-parametric estimators for the proposed measure are included.

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Section: Discussionmentioning

confidence: 99%

Some properties of cumulative extropy and its dynamic past version

Mohamed

2022

Filomat

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“…Finally, we can apply those models to ordered variables and their concomitants for future work. For more details, see [ 17 , 18 , 19 ]. For applications, see [ 20 , 21 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

Residual and Past Discrete Tsallis and Renyi Extropy with an Application to Softmax Function

Jawa

Fatima

Sayed-Ahmed

et al. 2022

Entropy

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In this paper, based on the discrete lifetime distribution, the residual and past of the Tsallis and Renyi extropy are introduced as new measures of information. Moreover, some of their properties and their relation to other measures are discussed. Furthermore, an example of a uniform distribution of the obtained models is given. Moreover, the softmax function can be used as a discrete probability distribution function with a unity sum. Thus, applying those measures to the softmax function for simulated and real data is demonstrated. Besides, for real data, the softmax data are fit to a convenient ARIMA model.

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“…In addition, they have almost the same value. In future work, we can extend this work to various types of measures such as Tsallis entropy; for more details, see [18] , [19] , [20] , [21] , [22] , [23] , [24] .…”

Section: Discussionmentioning

confidence: 99%