{"title":"毛刺X分布形状参数估计的极大极小缩小技术","authors":"A. N. Salman, Maymona M. Ameen, A. Abdul-Nabi","doi":"10.30526/2017.IHSCICONF.1820","DOIUrl":null,"url":null,"abstract":"The present paper concern with minimax shrinkage estimator technique in order to estimate Burr X distribution shape parameter, when prior information about the real shape obtainable as original estimate while known scale parameter. \n Derivation for Bias Ratio, Mean squared error and the Relative Efficiency equations. \n Numerical results and conclusions for the expressions mentioned above were displayed. Comparisons for proposed estimator with most recent works were made.","PeriodicalId":13236,"journal":{"name":"Ibn Al-Haitham Journal For Pure And Applied Science","volume":"42 1","pages":"484-490"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Minimax Shrunken Technique for Estimate Burr X Distribution Shape Parameter\",\"authors\":\"A. N. Salman, Maymona M. Ameen, A. Abdul-Nabi\",\"doi\":\"10.30526/2017.IHSCICONF.1820\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The present paper concern with minimax shrinkage estimator technique in order to estimate Burr X distribution shape parameter, when prior information about the real shape obtainable as original estimate while known scale parameter. \\n Derivation for Bias Ratio, Mean squared error and the Relative Efficiency equations. \\n Numerical results and conclusions for the expressions mentioned above were displayed. Comparisons for proposed estimator with most recent works were made.\",\"PeriodicalId\":13236,\"journal\":{\"name\":\"Ibn Al-Haitham Journal For Pure And Applied Science\",\"volume\":\"42 1\",\"pages\":\"484-490\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ibn Al-Haitham Journal For Pure And Applied Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30526/2017.IHSCICONF.1820\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ibn Al-Haitham Journal For Pure And Applied Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30526/2017.IHSCICONF.1820","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Minimax Shrunken Technique for Estimate Burr X Distribution Shape Parameter
The present paper concern with minimax shrinkage estimator technique in order to estimate Burr X distribution shape parameter, when prior information about the real shape obtainable as original estimate while known scale parameter.
Derivation for Bias Ratio, Mean squared error and the Relative Efficiency equations.
Numerical results and conclusions for the expressions mentioned above were displayed. Comparisons for proposed estimator with most recent works were made.
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