{"title":"不同损失函数下指数分布均值的新收缩熵估计","authors":"Priyanka Sahni, Raj Kumar","doi":"10.26713/cma.v14i1.1912","DOIUrl":null,"url":null,"abstract":". In this paper, a new shrinkage estimator of entropy function for mean of an exponential distribution is proposed. A progressive type censored sample is taken to obtain the estimator. For the new estimator, risk functions and relative risk functions are developed under symmetric and asymmetric loss functions, viz. squared error loss function and LINEX loss function, and new estimator is shown to have better performance than a classical estimator in terms of relative risk","PeriodicalId":43490,"journal":{"name":"Communications in Mathematics and Applications","volume":"IM-30 3","pages":""},"PeriodicalIF":0.2000,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"New Shrinkage Entropy Estimator for Mean of Exponential Distribution under Different Loss Functions\",\"authors\":\"Priyanka Sahni, Raj Kumar\",\"doi\":\"10.26713/cma.v14i1.1912\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". In this paper, a new shrinkage estimator of entropy function for mean of an exponential distribution is proposed. A progressive type censored sample is taken to obtain the estimator. For the new estimator, risk functions and relative risk functions are developed under symmetric and asymmetric loss functions, viz. squared error loss function and LINEX loss function, and new estimator is shown to have better performance than a classical estimator in terms of relative risk\",\"PeriodicalId\":43490,\"journal\":{\"name\":\"Communications in Mathematics and Applications\",\"volume\":\"IM-30 3\",\"pages\":\"\"},\"PeriodicalIF\":0.2000,\"publicationDate\":\"2023-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications in Mathematics and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.26713/cma.v14i1.1912\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications in Mathematics and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26713/cma.v14i1.1912","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS","Score":null,"Total":0}
New Shrinkage Entropy Estimator for Mean of Exponential Distribution under Different Loss Functions
. In this paper, a new shrinkage estimator of entropy function for mean of an exponential distribution is proposed. A progressive type censored sample is taken to obtain the estimator. For the new estimator, risk functions and relative risk functions are developed under symmetric and asymmetric loss functions, viz. squared error loss function and LINEX loss function, and new estimator is shown to have better performance than a classical estimator in terms of relative risk
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