article · Axioms · 2023
Excess Lifetime Extropy of Order Statistics
Mansour Shrahili, Mohamed Kayid
Cited by 10Open access
Abstract
This paper explores the concept of residual extropy as an uncertainty measure for order statistics. We specifically derive the residual extropy for the ith-order statistic and establish its relationship with the residual extropy of the ith-order statistic from a random sample generated from a uniform distribution. By employing this approach, we obtain a formula for the residual extropy of order statistics applicable to general continuous distributions. In addition, we offer two lower bounds that can be applied in situations where obtaining closed-form expressions for the residual extropy of order statistics in diverse distributions proves to be challenging. Additionally, we investigate the monotonicity properties of the residual extropy of order statistics. Furthermore, we present other aspects of the residual extropy of order statistics, including its dependence on the position of order statistics and various features of the underlying distribution.
Cite this paper
Shrahili, M., & Kayid, M. (2023). Excess Lifetime Extropy of Order Statistics. Axioms, 12(11), 1024. https://doi.org/10.3390/axioms12111024
Shrahili, Mansour, and Mohamed Kayid. “Excess Lifetime Extropy of Order Statistics.” Axioms, vol. 12, no. 11, 2023, p. 1024, https://doi.org/10.3390/axioms12111024.
Shrahili, Mansour, and Mohamed Kayid. 2023. “Excess Lifetime Extropy of Order Statistics.” Axioms 12 (11): 1024. https://doi.org/10.3390/axioms12111024.
Shrahili, M. and Kayid, M. (2023) ‘Excess Lifetime Extropy of Order Statistics’, Axioms, 12(11), p. 1024. Available at: https://doi.org/10.3390/axioms12111024.
@article{shrahili2023excess,
title = {{Excess Lifetime Extropy of Order Statistics}},
author = {Shrahili, Mansour and Kayid, Mohamed},
journal = {Axioms},
volume = {12},
number = {11},
pages = {1024},
year = {2023},
doi = {10.3390/axioms12111024}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.