THE BETA EXPONENTIATED WEIBULL GEOMETRIC DISTRIBUTION: MODELING, STRUCTURAL PROPERTIES, ESTIMATION AND AN APPLICATION TO A CERVICAL INTRAEPITHELIAL NEOPLASIA DATASET
{"title":"THE BETA EXPONENTIATED WEIBULL GEOMETRIC DISTRIBUTION: MODELING, STRUCTURAL PROPERTIES, ESTIMATION AND AN APPLICATION TO A CERVICAL INTRAEPITHELIAL NEOPLASIA DATASET","authors":"F. Louzada, I. Elbatal, D. Granzotto","doi":"10.28951/RBB.V36I4.329","DOIUrl":null,"url":null,"abstract":"A new distribution, the so called beta exponentiated Weibull geometric (BEWG) distribution is proposed. The new distribution is generated from the logit of a beta random variable and includes the exponentiated Weibull geometric distribution as particular case. Various structural properties including explicit expressions for the moments, moment generating function, mean deviation of the new distribution are derived. The estimation of the model parameters is performed by maximum likelihood method. The usefulness of the model was showed by using a real dataset. In order to validate the results a simulation bootstrap is presented in this paper.","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":"42 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista Brasileira de Biometria","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.28951/RBB.V36I4.329","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 1
Abstract
A new distribution, the so called beta exponentiated Weibull geometric (BEWG) distribution is proposed. The new distribution is generated from the logit of a beta random variable and includes the exponentiated Weibull geometric distribution as particular case. Various structural properties including explicit expressions for the moments, moment generating function, mean deviation of the new distribution are derived. The estimation of the model parameters is performed by maximum likelihood method. The usefulness of the model was showed by using a real dataset. In order to validate the results a simulation bootstrap is presented in this paper.