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Título: A new truncated lindley-generated family of distributions : properties, regression analysis, and applications
Autor(es): Hussein, Mohamed
Rodrigues, Gabriela Maria
Ortega, Edwin M. M.
Vila, Roberto
Elsayed, Howaida
ORCID: https://orcid.org/0000-0002-7332-0334
https://orcid.org/0000-0002-1985-8141
https://orcid.org/0000-0003-3999-7402
https://orcid.org/0000-0003-1073-0114
https://orcid.org/0000-0003-1323-5346
Afiliação do autor: Alexandria University, Department of Mathematics and Computer Science
King Khalid University, College of Business, Department of Business Administration
University of São Paulo, Piracicaba, Department of Exact Sciences
University of Brasilia, Department of Statistics
King Khalid University, College of Business, Department of Business Administration
Assunto: Dados censurados
Análise de sobrevivência
Máxima verossimilhança
Covid-19
Data de publicação: 2023
Editora: MDPI
Referência: HUSSEIN, Mohamed et al. A new truncated lindley-generated family of distributions: properties, regression analysis, and applications. Entropy, [S. l.], v. 25, n. 9, 1359, 2023. DOI: https://doi.org/10.3390/e25091359. Disponível em: https://www.mdpi.com/1099-4300/25/9/1359. Acesso em: 25 jun. 2024.
Abstract: We present the truncated Lindley-G (TLG) model, a novel class of probability distributions with an additional shape parameter, by composing a unit distribution called the truncated Lindley distribution with a parent distribution function 𝐺(𝑥). The proposed model’s characteristics including critical points, moments, generating function, quantile function, mean deviations, and entropy are discussed. Also, we introduce a regression model based on the truncated Lindley–Weibull distribution considering two systematic components. The model parameters are estimated using the maximum likelihood method. In order to investigate the behavior of the estimators, some simulations are run for various parameter settings, censoring percentages, and sample sizes. Four real datasets are used to demonstrate the new model’s potential.
Unidade Acadêmica: Instituto de Ciências Exatas (IE)
Departamento de Estatística (IE EST)
Licença: © 2023 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/).
DOI: https://doi.org/10.3390/e25091359
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