Type I Half Logistic Burr X-G Family: Properties, Bayesian, and Non-Bayesian Estimation under Censored Samples and Applications to COVID-19 Data

In this paper, we present a new family of continuous distributions known as the type I half logistic Burr X-G. The proposed family’s essential mathematical properties, such as quantile function (QuFu), moments (Mo), incomplete moments (InMo), mean deviation (MeD), Lorenz (Lo) and Bonferroni (Bo) cur...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021-10, Vol.2021, p.1-21
Main Authors: Algarni, Ali, M. Almarashi, Abdullah, Elbatal, I., S. Hassan, Amal, Almetwally, Ehab M., M. Daghistani, Abdulkader, Elgarhy, Mohammed
Format: Article
Language:English
Subjects:
Bayesian analysis
Coronaviruses
COVID-19
Engineering
Flexibility
Monte Carlo simulation
Parameter estimation
Random variables
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Summary:In this paper, we present a new family of continuous distributions known as the type I half logistic Burr X-G. The proposed family’s essential mathematical properties, such as quantile function (QuFu), moments (Mo), incomplete moments (InMo), mean deviation (MeD), Lorenz (Lo) and Bonferroni (Bo) curves, and entropy (En), are provided. Special models of the family are presented, including type I half logistic Burr X-Lomax, type I half logistic Burr X-Rayleigh, and type I half logistic Burr X-exponential. The maximum likelihood (MLL) and Bayesian techniques are utilized to produce parameter estimators for the recommended family using type II censored data. Monte Carlo simulation is used to evaluate the accuracy of estimates for one of the family’s special models. The COVID-19 real datasets from Italy, Canada, and Belgium are analysed to demonstrate the significance and flexibility of some new distributions from the family.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/5461130