TY - JOUR
T1 - A flexible extension to an extreme distribution
AU - Eliwa, Mohamed S.
AU - Alshammari, Fahad Sameer
AU - Abualnaja, Khadijah M.
AU - El-Morshedy, Mahmoud
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
PY - 2021/5
Y1 - 2021/5
N2 - The aim of this paper is not only to propose a new extreme distribution, but also to show that the new extreme model can be used as an alternative to well-known distributions in the literature to model various kinds of datasets in different fields. Several of its statistical properties are explored. It is found that the new extreme model can be utilized for modeling both asymmetric and symmetric datasets, which suffer from over-and under-dispersed phenomena. Moreover, the hazard rate function can be constant, increasing, increasing–constant, or unimodal shaped. The maximum likelihood method is used to estimate the model parameters based on complete and censored samples. Finally, a significant amount of simulations was conducted along with real data applications to illustrate the use of the new extreme distribution.
AB - The aim of this paper is not only to propose a new extreme distribution, but also to show that the new extreme model can be used as an alternative to well-known distributions in the literature to model various kinds of datasets in different fields. Several of its statistical properties are explored. It is found that the new extreme model can be utilized for modeling both asymmetric and symmetric datasets, which suffer from over-and under-dispersed phenomena. Moreover, the hazard rate function can be constant, increasing, increasing–constant, or unimodal shaped. The maximum likelihood method is used to estimate the model parameters based on complete and censored samples. Finally, a significant amount of simulations was conducted along with real data applications to illustrate the use of the new extreme distribution.
KW - Censored samples
KW - Hazard rate function
KW - Maximum likelihood estimation
KW - Probability distributions
KW - Skewed and symmetric data
UR - https://www.scopus.com/pages/publications/85105731652
U2 - 10.3390/sym13050745
DO - 10.3390/sym13050745
M3 - Article
AN - SCOPUS:85105731652
SN - 2073-8994
VL - 13
JO - Symmetry
JF - Symmetry
IS - 5
M1 - 745
ER -