{"title":"指数Gumbel分布中参数的贝叶斯估计","authors":"Gholamhossein Gholami","doi":"10.18869/ACADPUB.JSRI.13.2.181","DOIUrl":null,"url":null,"abstract":"The Exponentiated Gumbel (EG) distribution has been proposed to capture some aspects of the data that the Gumbel distribution fails to specify. In this paper, we estimate the EG’s parameters in the Bayesian framework. We consider a 2-level hierarchical structure for prior distribution. As the posterior distributions do not admit a closed form, we do an approximated inference by using Gibbs and Metropolis-Hastings algorithm.","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Bayesian Estimation of Parameters in the Exponentiated Gumbel Distribution\",\"authors\":\"Gholamhossein Gholami\",\"doi\":\"10.18869/ACADPUB.JSRI.13.2.181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Exponentiated Gumbel (EG) distribution has been proposed to capture some aspects of the data that the Gumbel distribution fails to specify. In this paper, we estimate the EG’s parameters in the Bayesian framework. We consider a 2-level hierarchical structure for prior distribution. As the posterior distributions do not admit a closed form, we do an approximated inference by using Gibbs and Metropolis-Hastings algorithm.\",\"PeriodicalId\":422124,\"journal\":{\"name\":\"Journal of Statistical Research of Iran\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Statistical Research of Iran\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18869/ACADPUB.JSRI.13.2.181\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Research of Iran","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18869/ACADPUB.JSRI.13.2.181","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bayesian Estimation of Parameters in the Exponentiated Gumbel Distribution
The Exponentiated Gumbel (EG) distribution has been proposed to capture some aspects of the data that the Gumbel distribution fails to specify. In this paper, we estimate the EG’s parameters in the Bayesian framework. We consider a 2-level hierarchical structure for prior distribution. As the posterior distributions do not admit a closed form, we do an approximated inference by using Gibbs and Metropolis-Hastings algorithm.
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