Kethamile Rannona, B. Oluyede, Fastel Chipepa, B. Makubate
{"title":"The exponentiated odd exponential half logistic-G power series class of distributions with applications","authors":"Kethamile Rannona, B. Oluyede, Fastel Chipepa, B. Makubate","doi":"10.1080/09720510.2021.1984570","DOIUrl":null,"url":null,"abstract":"Abstract We propose a new generalized class of distributions called the Exponentiated odd Exponential Half Logistic-G Power Series (EOEHL-GPS) class of distributions. This model is obtained by compounding the Exponentiated odd Exponential Half Logistic-G distribution with the power series distribution. Statistical properties of the EOEHL-GPS class of distributions are discussed. Maximum likelihood estimates for the proposed model were obtained. A simulation study was carried out to assess the performance of the maximum likelihood estimates. Finally, real data examples are used to illustrate the usefulness of the new model compared to other models.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistics and Management Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/09720510.2021.1984570","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract We propose a new generalized class of distributions called the Exponentiated odd Exponential Half Logistic-G Power Series (EOEHL-GPS) class of distributions. This model is obtained by compounding the Exponentiated odd Exponential Half Logistic-G distribution with the power series distribution. Statistical properties of the EOEHL-GPS class of distributions are discussed. Maximum likelihood estimates for the proposed model were obtained. A simulation study was carried out to assess the performance of the maximum likelihood estimates. Finally, real data examples are used to illustrate the usefulness of the new model compared to other models.