TY - JOUR
T1 - Data analysis for COVID-19 deaths using a novel statistical model
T2 - Simulation and fuzzy application
AU - El-Sherpieny, El Sayed A.
AU - Almetwally, Ehab M.
AU - Muse, Abdisalam Hassan
AU - Hussam, Eslam
N1 - Publisher Copyright:
© 2023 El-Sherpieny et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2023/4
Y1 - 2023/4
N2 - This paper provides a novel model that is more relevant than the well-known conventional distributions, which stand for the two-parameter distribution of the lifetime modified Kies Topp–Leone (MKTL) model. Compared to the current distributions, the most recent one gives an unusually varied collection of probability functions. The density and hazard rate functions exhibit features, demonstrating that the model is flexible to several kinds of data. Multiple statistical characteristics have been obtained. To estimate the parameters of the MKTL model, we employed various estimation techniques, including maximum likelihood estimators (MLEs) and the Bayesian estimation approach. We compared the traditional reliability function model to the fuzzy reliability function model within the reliability analysis framework. A complete Monte Carlo simulation analysis is conducted to determine the precision of these estimators. The suggested model outperforms competing models in real-world applications and may be chosen as an enhanced model for building a statistical model for the COVID-19 data and other data sets with similar features.
AB - This paper provides a novel model that is more relevant than the well-known conventional distributions, which stand for the two-parameter distribution of the lifetime modified Kies Topp–Leone (MKTL) model. Compared to the current distributions, the most recent one gives an unusually varied collection of probability functions. The density and hazard rate functions exhibit features, demonstrating that the model is flexible to several kinds of data. Multiple statistical characteristics have been obtained. To estimate the parameters of the MKTL model, we employed various estimation techniques, including maximum likelihood estimators (MLEs) and the Bayesian estimation approach. We compared the traditional reliability function model to the fuzzy reliability function model within the reliability analysis framework. A complete Monte Carlo simulation analysis is conducted to determine the precision of these estimators. The suggested model outperforms competing models in real-world applications and may be chosen as an enhanced model for building a statistical model for the COVID-19 data and other data sets with similar features.
UR - http://www.scopus.com/inward/record.url?scp=85152096265&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0283618
DO - 10.1371/journal.pone.0283618
M3 - Article
C2 - 37036849
AN - SCOPUS:85152096265
SN - 1932-6203
VL - 18
JO - PLoS ONE
JF - PLoS ONE
IS - 4 April
M1 - e0283618
ER -
