{"title":"A generalized Birnbaum-Saunders distribution with application to the air pollution data","authors":"M. Tamandi, A. Jamalizadeh, M. Mahdizadeh","doi":"10.1285/I20705948V12N1P26","DOIUrl":null,"url":null,"abstract":"Birnbaum-Saunders (BS) distribution is a model with positive domain thatis used in many fields including reliability and environmental studies. Thisarticle introduces a generalized version of the BS distribution which arisesfrom the shape mixture of skew normal distribution. A feasible EM typealgorithm is developed to obtain maximum likelihood (ML) estimates of pa-rameters of the new model. The asymptotic standard errors of ML estimatesare obtained via the information-based approximation. The robustness andapplication of the proposed methodology is illustrated through simulationstudies and air pollution analysis.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"12 1","pages":"26-43"},"PeriodicalIF":0.6000,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1285/I20705948V12N1P26","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronic Journal of Applied Statistical Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1285/I20705948V12N1P26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 4
Abstract
Birnbaum-Saunders (BS) distribution is a model with positive domain thatis used in many fields including reliability and environmental studies. Thisarticle introduces a generalized version of the BS distribution which arisesfrom the shape mixture of skew normal distribution. A feasible EM typealgorithm is developed to obtain maximum likelihood (ML) estimates of pa-rameters of the new model. The asymptotic standard errors of ML estimatesare obtained via the information-based approximation. The robustness andapplication of the proposed methodology is illustrated through simulationstudies and air pollution analysis.