A new lifetime family of distributions: Theoretical developments and analysis of COVID 19 data

In parametric statistical modeling and inference, it is critical to develop generalizations of existing statistical distributions to make them more flexible in modeling real data sets. Thus , this paper contributes to the subject by investigating a new flexible and versatile generalized family of di...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	I. Elbatal
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Beta G family Topp–Leone G family Moments Entropy MCMC Symmetric and asymmetric loss functions Physics QC1-999
Acceso en línea:	https://doaj.org/article/4c067da217b547e1b17079cb727b18b1
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4c067da217b547e1b17079cb727b18b1
record_format	dspace
spelling	oai:doaj.org-article:4c067da217b547e1b17079cb727b18b12021-11-20T05:05:43ZA new lifetime family of distributions: Theoretical developments and analysis of COVID 19 data2211-379710.1016/j.rinp.2021.104979https://doaj.org/article/4c067da217b547e1b17079cb727b18b12021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2211379721009888https://doaj.org/toc/2211-3797In parametric statistical modeling and inference, it is critical to develop generalizations of existing statistical distributions to make them more flexible in modeling real data sets. Thus , this paper contributes to the subject by investigating a new flexible and versatile generalized family of distributions defined from the alliance of the families known as beta-G and Topp–Leone generated (TL-G), inspiring the name of BTL-G family. The characteristics of this new family are studied through analytical, graphical and numerical approaches. Statistical features of the family such as expansion of density function (pdf), cumulative function (cdf), moments (MOs), incomplete moments (IMOs), mean deviation (MDE), and entropy (ENT) are calculated. The model parameters’ maximum likelihood estimates (MaxLEs) and Bayesian estimates (BEs) are provided. Symmetric and Asymmetric Bayesian Loss function have been discussed. A complete simulation study is proposed to illustrate their numerical efficiency. The considered model is also applied to analyze two different kinds of genuine COVID 19 data sets. We show that it outperforms other well-known models defined with the same baseline distribution, proving its high level of adaptability in the context of data analysis.I. ElbatalElsevierarticleBeta G familyTopp–Leone G familyMomentsEntropyMCMCSymmetric and asymmetric loss functionsPhysicsQC1-999ENResults in Physics, Vol 31, Iss , Pp 104979- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Beta G family Topp–Leone G family Moments Entropy MCMC Symmetric and asymmetric loss functions Physics QC1-999
spellingShingle	Beta G family Topp–Leone G family Moments Entropy MCMC Symmetric and asymmetric loss functions Physics QC1-999 I. Elbatal A new lifetime family of distributions: Theoretical developments and analysis of COVID 19 data
description	In parametric statistical modeling and inference, it is critical to develop generalizations of existing statistical distributions to make them more flexible in modeling real data sets. Thus , this paper contributes to the subject by investigating a new flexible and versatile generalized family of distributions defined from the alliance of the families known as beta-G and Topp–Leone generated (TL-G), inspiring the name of BTL-G family. The characteristics of this new family are studied through analytical, graphical and numerical approaches. Statistical features of the family such as expansion of density function (pdf), cumulative function (cdf), moments (MOs), incomplete moments (IMOs), mean deviation (MDE), and entropy (ENT) are calculated. The model parameters’ maximum likelihood estimates (MaxLEs) and Bayesian estimates (BEs) are provided. Symmetric and Asymmetric Bayesian Loss function have been discussed. A complete simulation study is proposed to illustrate their numerical efficiency. The considered model is also applied to analyze two different kinds of genuine COVID 19 data sets. We show that it outperforms other well-known models defined with the same baseline distribution, proving its high level of adaptability in the context of data analysis.
format	article
author	I. Elbatal
author_facet	I. Elbatal
author_sort	I. Elbatal
title	A new lifetime family of distributions: Theoretical developments and analysis of COVID 19 data
title_short	A new lifetime family of distributions: Theoretical developments and analysis of COVID 19 data
title_full	A new lifetime family of distributions: Theoretical developments and analysis of COVID 19 data
title_fullStr	A new lifetime family of distributions: Theoretical developments and analysis of COVID 19 data
title_full_unstemmed	A new lifetime family of distributions: Theoretical developments and analysis of COVID 19 data
title_sort	new lifetime family of distributions: theoretical developments and analysis of covid 19 data
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/4c067da217b547e1b17079cb727b18b1
work_keys_str_mv	AT ielbatal anewlifetimefamilyofdistributionstheoreticaldevelopmentsandanalysisofcovid19data AT ielbatal newlifetimefamilyofdistributionstheoreticaldevelopmentsandanalysisofcovid19data
_version_	1718419594862919680

A new lifetime family of distributions: Theoretical developments and analysis of COVID 19 data

Ejemplares similares