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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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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. |
format | Online Article Text |
id | pubmed-10085036 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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