{"title":"E-Bayesian and Hierarchical Bayesian Estimation of Inverse Rayleigh Distribution","authors":"R. B. Athirakrishnan, E. I. Abdul-Sathar","doi":"10.1080/01966324.2021.1914250","DOIUrl":null,"url":null,"abstract":"Abstract This article proposes E-Bayesian and Hierarchical Bayesian estimation method to estimate the scale parameter and reversed hazard rate of inverse Rayleigh distribution. These estimators are derived under squared error, entropy and precautionary loss functions. The definition and properties of proposed estimators are given. The proposed estimators are suitable for all sample sizes and perform better than the existing classical estimator, such as MLE, with high efficiency. Simulated and real data sets are also discussed for studying the performance of the estimators, which shows that the proposed estimators are efficient and easy to use.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"41 1","pages":"70 - 87"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01966324.2021.1914250","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Mathematical and Management Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/01966324.2021.1914250","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Business, Management and Accounting","Score":null,"Total":0}
引用次数: 14
Abstract
Abstract This article proposes E-Bayesian and Hierarchical Bayesian estimation method to estimate the scale parameter and reversed hazard rate of inverse Rayleigh distribution. These estimators are derived under squared error, entropy and precautionary loss functions. The definition and properties of proposed estimators are given. The proposed estimators are suitable for all sample sizes and perform better than the existing classical estimator, such as MLE, with high efficiency. Simulated and real data sets are also discussed for studying the performance of the estimators, which shows that the proposed estimators are efficient and easy to use.