Titre:	A new truncated lindley-generated family of distributions : properties, regression analysis, and applications
Auteur(s):	Hussein, Mohamed Rodrigues, Gabriela Maria Ortega, Edwin M. M. Vila, Roberto Elsayed, Howaida
metadata.dc.identifier.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
metadata.dc.contributor.affiliation:	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
Date de publication:	2023
Editeur:	MDPI
Référence bibliographique:	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.
metadata.dc.description.unidade:	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
Collection(s) :	Artigos publicados em periódicos e afins UnB - Covid-19

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