Exponentiated Generalized Weibull Exponential Distribution: Properties, Estimation and Applications

Real-life sciences rely heavily on statistical modeling because new applications and phenomena pop up constantly, increasing the demand for new distributions. In this article, the exponentiated generalized Weibull exponential (EGWE) distribution is proposed and studied. The density can exhibit de...

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Main Authors: Anuwoje Ida L. Abonongo, John Abonongo
Format: Article
Language:English
Published: The Scientific Association for Studies and Applied Research 2024-04-01
Series:Computational Journal of Mathematical and Statistical Sciences
Subjects:
Online Access:https://cjmss.journals.ekb.eg/article_328917.html
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author Anuwoje Ida L. Abonongo
John Abonongo
author_facet Anuwoje Ida L. Abonongo
John Abonongo
author_sort Anuwoje Ida L. Abonongo
collection DOAJ
description Real-life sciences rely heavily on statistical modeling because new applications and phenomena pop up constantly, increasing the demand for new distributions. In this article, the exponentiated generalized Weibull exponential (EGWE) distribution is proposed and studied. The density can exhibit decreasing, increasing, right-skewed, and left-skewed shapes. The hazard rate function shows decreasing, J-shaped, bathtub, and upside-down bathtub shapes. Statistical properties such as asymptotic behavior, quantile function, moment and incomplete moments, mean and median deviations, inequality measures, moment generating function, and order statistics are studied. The estimation of the parameters of the EGWE distribution using six frequentist estimation methods, namely maximum likelihood, least squares, maximum product spacing, weighted least squares, Anderson-Darling, and Cramer-von Mises are discussed. Monte Carlo simulation study to ascertain the behavior of the estimators in terms of average absolute biases and mean square error is carried out. All the estimators performed very well since the average absolute biases and mean square errors decrease as the sample size increases. The usefulness of the EGWE distribution is illustrated with two datasets. The results show that the EGWE distribution provides better parametric fit compared with the competing distributions. Also, in estimating the EGWE parameters with the six estimation methods, the results show that the performance of the six estimation methods is good.
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spelling doaj-art-d633e0a20b8c41968bce33f4d1d8a6202025-08-20T03:40:54ZengThe Scientific Association for Studies and Applied ResearchComputational Journal of Mathematical and Statistical Sciences2974-34352974-34432024-04-0131758410.21608/CJMSS.2023.243845.1023Exponentiated Generalized Weibull Exponential Distribution: Properties, Estimation and ApplicationsAnuwoje Ida L. Abonongo0John Abonongo 1Department of Statistics and Actuarial Science, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, GhanaDepartment of Statistics and Actuarial Science, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, GhanaReal-life sciences rely heavily on statistical modeling because new applications and phenomena pop up constantly, increasing the demand for new distributions. In this article, the exponentiated generalized Weibull exponential (EGWE) distribution is proposed and studied. The density can exhibit decreasing, increasing, right-skewed, and left-skewed shapes. The hazard rate function shows decreasing, J-shaped, bathtub, and upside-down bathtub shapes. Statistical properties such as asymptotic behavior, quantile function, moment and incomplete moments, mean and median deviations, inequality measures, moment generating function, and order statistics are studied. The estimation of the parameters of the EGWE distribution using six frequentist estimation methods, namely maximum likelihood, least squares, maximum product spacing, weighted least squares, Anderson-Darling, and Cramer-von Mises are discussed. Monte Carlo simulation study to ascertain the behavior of the estimators in terms of average absolute biases and mean square error is carried out. All the estimators performed very well since the average absolute biases and mean square errors decrease as the sample size increases. The usefulness of the EGWE distribution is illustrated with two datasets. The results show that the EGWE distribution provides better parametric fit compared with the competing distributions. Also, in estimating the EGWE parameters with the six estimation methods, the results show that the performance of the six estimation methods is good. https://cjmss.journals.ekb.eg/article_328917.htmlweibull distributionestimation methodsbathtubexponentiated generalizedsimulations
spellingShingle Anuwoje Ida L. Abonongo
John Abonongo
Exponentiated Generalized Weibull Exponential Distribution: Properties, Estimation and Applications
Computational Journal of Mathematical and Statistical Sciences
weibull distribution
estimation methods
bathtub
exponentiated generalized
simulations
title Exponentiated Generalized Weibull Exponential Distribution: Properties, Estimation and Applications
title_full Exponentiated Generalized Weibull Exponential Distribution: Properties, Estimation and Applications
title_fullStr Exponentiated Generalized Weibull Exponential Distribution: Properties, Estimation and Applications
title_full_unstemmed Exponentiated Generalized Weibull Exponential Distribution: Properties, Estimation and Applications
title_short Exponentiated Generalized Weibull Exponential Distribution: Properties, Estimation and Applications
title_sort exponentiated generalized weibull exponential distribution properties estimation and applications
topic weibull distribution
estimation methods
bathtub
exponentiated generalized
simulations
url https://cjmss.journals.ekb.eg/article_328917.html
work_keys_str_mv AT anuwojeidalabonongo exponentiatedgeneralizedweibullexponentialdistributionpropertiesestimationandapplications
AT johnabonongo exponentiatedgeneralizedweibullexponentialdistributionpropertiesestimationandapplications