{"title":"Some Properties and Applications of the Gompertz Topp-Leone Inverse Exponential Distribution","authors":"Sahib S. Hammed, M. Khaleel","doi":"10.55562/jrucs.v54i1.607","DOIUrl":null,"url":null,"abstract":"The Gompertz Topp Leone Inverse Exponential (GoTLIE) distribution, which has three parameters, is a novel distribution that we introduce in this article. Inverse exponential (IE) distributions with various parameter values can be combined to form the new distribution. The moments, moment generating function, quantile function, reliability function, and hazard function are only a few of the crucial structural aspects of the new model that are derived. We utilize the approach of greatest likelihood to estimate the new distribution's parameters (MLE). To show how flexible the new distribution is, we utilized two real data used to demonstrate the GoTLIE distribution.","PeriodicalId":14899,"journal":{"name":"Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 )","volume":"63 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 )","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55562/jrucs.v54i1.607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Gompertz Topp Leone Inverse Exponential (GoTLIE) distribution, which has three parameters, is a novel distribution that we introduce in this article. Inverse exponential (IE) distributions with various parameter values can be combined to form the new distribution. The moments, moment generating function, quantile function, reliability function, and hazard function are only a few of the crucial structural aspects of the new model that are derived. We utilize the approach of greatest likelihood to estimate the new distribution's parameters (MLE). To show how flexible the new distribution is, we utilized two real data used to demonstrate the GoTLIE distribution.