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Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application

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 collectio...

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Autores principales: El-Sherpieny, El-Sayed A., Almetwally, Ehab M., Muse, Abdisalam Hassan, Hussam, Eslam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10085036/
https://www.ncbi.nlm.nih.gov/pubmed/37036849
http://dx.doi.org/10.1371/journal.pone.0283618
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author El-Sherpieny, El-Sayed A.
Almetwally, Ehab M.
Muse, Abdisalam Hassan
Hussam, Eslam
author_facet El-Sherpieny, El-Sayed A.
Almetwally, Ehab M.
Muse, Abdisalam Hassan
Hussam, Eslam
author_sort El-Sherpieny, El-Sayed A.
collection PubMed
description 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.
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spelling pubmed-100850362023-04-11 Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application El-Sherpieny, El-Sayed A. Almetwally, Ehab M. Muse, Abdisalam Hassan Hussam, Eslam PLoS One Research Article 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. Public Library of Science 2023-04-10 /pmc/articles/PMC10085036/ /pubmed/37036849 http://dx.doi.org/10.1371/journal.pone.0283618 Text en © 2023 El-Sherpieny et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
El-Sherpieny, El-Sayed A.
Almetwally, Ehab M.
Muse, Abdisalam Hassan
Hussam, Eslam
Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application
title Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application
title_full Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application
title_fullStr Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application
title_full_unstemmed Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application
title_short Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application
title_sort data analysis for covid-19 deaths using a novel statistical model: simulation and fuzzy application
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10085036/
https://www.ncbi.nlm.nih.gov/pubmed/37036849
http://dx.doi.org/10.1371/journal.pone.0283618
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