TY - JOUR
T1 - Alpha Power Generalized Inverse Rayleigh Distribution
T2 - Its Properties and Applications
AU - Ali, Muhammad
AU - Khalil, Alamgir
AU - Almaspoor, Zahra
AU - Hussain, Sundus
AU - Khalil, Umair
AU - El-Morshedy, M.
N1 - Publisher Copyright:
© 2022 Muhammad Ali et al.
PY - 2022
Y1 - 2022
N2 - This manuscript is related with the development of Alpha Power Generalized Inverse Rayleigh (APGIR) Distribution. The suggested model provides fit of life time data more efficiently. Some of the important characteristics of the suggested model are obtained including moments, moment generating function, quantile, mode, order statistics, stress-strength parameter, and entropies. Parameter estimates are obtained by MLE technique. The performance of the suggested model is evaluated using real-world data sets. The findings of the simulation and real data sets suggest that the newly proposed model is superior to other current competitor models.
AB - This manuscript is related with the development of Alpha Power Generalized Inverse Rayleigh (APGIR) Distribution. The suggested model provides fit of life time data more efficiently. Some of the important characteristics of the suggested model are obtained including moments, moment generating function, quantile, mode, order statistics, stress-strength parameter, and entropies. Parameter estimates are obtained by MLE technique. The performance of the suggested model is evaluated using real-world data sets. The findings of the simulation and real data sets suggest that the newly proposed model is superior to other current competitor models.
UR - https://www.scopus.com/pages/publications/85132530934
U2 - 10.1155/2022/7847110
DO - 10.1155/2022/7847110
M3 - Article
AN - SCOPUS:85132530934
SN - 1024-123X
VL - 2022
JO - Mathematical Problems in Engineering
JF - Mathematical Problems in Engineering
M1 - 7847110
ER -