{"title":"An extended version of Kumaraswamy inverse Weibull distribution and its properties","authors":"C. Kumar, Subha R. Nair","doi":"10.6092/ISSN.1973-2201/6317","DOIUrl":null,"url":null,"abstract":"Here we consider an extended version of the Kumaraswamy modified inverse Weibull distribution and investigate some of its theoretical properties through deriving expressions for cumulative distribution function, reliability function, hazard rate function, quantile function, characteristic function, raw moments, median, mode etc. Certain reliability measures of the distribution are obtained along with the distribution and moments of its order statistics. The maximum likelihood estimation of the parameters of the distribution is discussed and certain real life data applications are given for illustrating the usefulness of the model. Further, with the help of simulated data sets it is shown that the average bias and mean square errors of the maximum likelihood estimators are in decreasing order as the sample size increases.","PeriodicalId":45117,"journal":{"name":"Statistica","volume":"76 1","pages":"249-272"},"PeriodicalIF":1.6000,"publicationDate":"2016-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.6092/ISSN.1973-2201/6317","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 4
Abstract
Here we consider an extended version of the Kumaraswamy modified inverse Weibull distribution and investigate some of its theoretical properties through deriving expressions for cumulative distribution function, reliability function, hazard rate function, quantile function, characteristic function, raw moments, median, mode etc. Certain reliability measures of the distribution are obtained along with the distribution and moments of its order statistics. The maximum likelihood estimation of the parameters of the distribution is discussed and certain real life data applications are given for illustrating the usefulness of the model. Further, with the help of simulated data sets it is shown that the average bias and mean square errors of the maximum likelihood estimators are in decreasing order as the sample size increases.