{"title":"时间截尾数据和失效截尾数据的扩展杰弗瑞先验信息Bayes估计","authors":"Haneen Reed Sahib, Hadeel Salim Al-Kutubi","doi":"10.31642/jokmc/2018/100208","DOIUrl":null,"url":null,"abstract":"In this research, the Bayes estimator was derived based on Time censored data of the first type, and the Failure censored data of the second type. Reliance has been made on extension of Jeffery prior information. Finally, the simulation was used based on the MATLAB program and with different inputs to find the best estimator among Maximum Likelihood estimator and Bayes estimators with extension that has the least mean percentage error","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"447 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayes estimators with extension of Jeffery prior information for Time censored data and Failure censored data\",\"authors\":\"Haneen Reed Sahib, Hadeel Salim Al-Kutubi\",\"doi\":\"10.31642/jokmc/2018/100208\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this research, the Bayes estimator was derived based on Time censored data of the first type, and the Failure censored data of the second type. Reliance has been made on extension of Jeffery prior information. Finally, the simulation was used based on the MATLAB program and with different inputs to find the best estimator among Maximum Likelihood estimator and Bayes estimators with extension that has the least mean percentage error\",\"PeriodicalId\":115908,\"journal\":{\"name\":\"Journal of Kufa for Mathematics and Computer\",\"volume\":\"447 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Kufa for Mathematics and Computer\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31642/jokmc/2018/100208\",\"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 Kufa for Mathematics and Computer","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31642/jokmc/2018/100208","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bayes estimators with extension of Jeffery prior information for Time censored data and Failure censored data
In this research, the Bayes estimator was derived based on Time censored data of the first type, and the Failure censored data of the second type. Reliance has been made on extension of Jeffery prior information. Finally, the simulation was used based on the MATLAB program and with different inputs to find the best estimator among Maximum Likelihood estimator and Bayes estimators with extension that has the least mean percentage error
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