Hesham Reyad, Farrukh Jamal, G. Hamedani, Soha Othman
{"title":"The Alpha Power Transformed Dagum Distribution: Properties and Applications","authors":"Hesham Reyad, Farrukh Jamal, G. Hamedani, Soha Othman","doi":"10.28924/ada/stat.1.108","DOIUrl":null,"url":null,"abstract":"In this study, we propose a new extension of the Dagum distribution called the alpha power transformed Dagum distribution. Basic statistical properties of the new distribution such as; quantile function, raw and incomplete moments, moment generating function, order statistics, Rényi entropy, stochastic ordering and stress strength model are investigated. The characterizations of the new model is investigated. The method of maximum likelihood is used to estimate the model parameters of the new distribution and the observed information matrix is also obtained. A Monte Carlo simulation is presented to examine the behavior of the parameter estimates. The applicability of the new model is demonstrated by means of three applications.","PeriodicalId":153849,"journal":{"name":"European Journal of Statistics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.28924/ada/stat.1.108","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
In this study, we propose a new extension of the Dagum distribution called the alpha power transformed Dagum distribution. Basic statistical properties of the new distribution such as; quantile function, raw and incomplete moments, moment generating function, order statistics, Rényi entropy, stochastic ordering and stress strength model are investigated. The characterizations of the new model is investigated. The method of maximum likelihood is used to estimate the model parameters of the new distribution and the observed information matrix is also obtained. A Monte Carlo simulation is presented to examine the behavior of the parameter estimates. The applicability of the new model is demonstrated by means of three applications.