The Generalization Inverse Weibull Distribution Related to X-Gamma Generator Family: Simulation and Application for Breast Cancer

The aim of this paper is to propose the new three-parameter X-Gamma inverse Weibull (XGAIW) distribution which generalizes the inverse Weibull model. The density function of the XGAIW can be expressed as a linear combination of the inverse Weibull densities. Some mathematical quantities (reliability...

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Main Author: R. Alshenawy
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Function Spaces
Online Access:http://dx.doi.org/10.1155/2022/4693490
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author R. Alshenawy
author_facet R. Alshenawy
author_sort R. Alshenawy
collection DOAJ
description The aim of this paper is to propose the new three-parameter X-Gamma inverse Weibull (XGAIW) distribution which generalizes the inverse Weibull model. The density function of the XGAIW can be expressed as a linear combination of the inverse Weibull densities. Some mathematical quantities (reliability and hazard rate properties) of the proposed XGAIW model are derived. Moreover, four estimation methods, namely, the maximum likelihood, maximum product spacing, least squares, and weighted least squares methods, are utilized to estimate the XGAIW parameters. The Monte Carlo simulation study has been performed to assess the performance of the proposed estimation methods using some criteria. The importance, flexibility, and potentiality of the XGAIW model are studied via a breast cancer data set application. The XGAIW model can produce better fits than some well-known distributions, so the proposed model can be used, as a good alternative to some existing distributions, in modeling several real data.
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spelling doaj-art-a4aab06e089342178e21f64e0b1a93ac2025-02-03T07:24:27ZengWileyJournal of Function Spaces2314-88882022-01-01202210.1155/2022/4693490The Generalization Inverse Weibull Distribution Related to X-Gamma Generator Family: Simulation and Application for Breast CancerR. Alshenawy0Department of Mathematics and StatisticsThe aim of this paper is to propose the new three-parameter X-Gamma inverse Weibull (XGAIW) distribution which generalizes the inverse Weibull model. The density function of the XGAIW can be expressed as a linear combination of the inverse Weibull densities. Some mathematical quantities (reliability and hazard rate properties) of the proposed XGAIW model are derived. Moreover, four estimation methods, namely, the maximum likelihood, maximum product spacing, least squares, and weighted least squares methods, are utilized to estimate the XGAIW parameters. The Monte Carlo simulation study has been performed to assess the performance of the proposed estimation methods using some criteria. The importance, flexibility, and potentiality of the XGAIW model are studied via a breast cancer data set application. The XGAIW model can produce better fits than some well-known distributions, so the proposed model can be used, as a good alternative to some existing distributions, in modeling several real data.http://dx.doi.org/10.1155/2022/4693490
spellingShingle R. Alshenawy
The Generalization Inverse Weibull Distribution Related to X-Gamma Generator Family: Simulation and Application for Breast Cancer
Journal of Function Spaces
title The Generalization Inverse Weibull Distribution Related to X-Gamma Generator Family: Simulation and Application for Breast Cancer
title_full The Generalization Inverse Weibull Distribution Related to X-Gamma Generator Family: Simulation and Application for Breast Cancer
title_fullStr The Generalization Inverse Weibull Distribution Related to X-Gamma Generator Family: Simulation and Application for Breast Cancer
title_full_unstemmed The Generalization Inverse Weibull Distribution Related to X-Gamma Generator Family: Simulation and Application for Breast Cancer
title_short The Generalization Inverse Weibull Distribution Related to X-Gamma Generator Family: Simulation and Application for Breast Cancer
title_sort generalization inverse weibull distribution related to x gamma generator family simulation and application for breast cancer
url http://dx.doi.org/10.1155/2022/4693490
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AT ralshenawy generalizationinverseweibulldistributionrelatedtoxgammageneratorfamilysimulationandapplicationforbreastcancer