Nonparametric estimation of distributions of order statistics with application to nuclear engineering

2015 ◽  
pp. 2657-2665
Author(s):  
C Butucea ◽  
J Delmas ◽  
A Dutfoy ◽  
R Fischer
Keyword(s):  
Order Statistics ◽  
Nonparametric Estimation ◽  
Nuclear Engineering ◽  
Estimation Of Distributions

scholarly journals Ordered Variables and Their Concomitants under Extropy via COVID-19 Data Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Mohamed S. Mohamed ◽  
Alanazi Talal Abdulrahman ◽  
Zahra Almaspoor ◽  
M. Yusuf
Keyword(s):  
Order Statistics ◽  
Nonparametric Estimation ◽  
Upper Bound ◽  
Real Data ◽  
The Other ◽  
Generalized Order Statistics ◽  
Stochastic Orders ◽  
Other Hand ◽  
Data Application ◽  
Generalized Order

Extropy, as a complementary dual of entropy, has been discussed in many works of literature, where it is declared for other measures as an extension of extropy. In this article, we obtain the extropy of generalized order statistics via its dual and give some examples from well-known distributions. Furthermore, we study the residual and past extropy for such models. On the other hand, based on Farlie–Gumbel–Morgenstern distribution, we consider the residual extropy of concomitants of m-generalized order statistics and present this measure with some additional features. In addition, we provide the upper bound and stochastic orders of it. Finally, nonparametric estimation of the residual extropy of concomitants of m-generalized order statistics is included using simulated and real data connected with COVID-19 virus.

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