{"title":"使用辅助信息的改进线性回归","authors":"Shabnum Gul","doi":"10.18782/2582-2845.8487","DOIUrl":null,"url":null,"abstract":"The present study was taken under consideration in order to propose improved linear regression using auxiliary information’s of coefficient of regression, coefficient of skewness, coefficient of variation in order to achieve more precision in estimates than the already existing estimators. The properties associated with the proposed estimators are assessed by mean square error and bias and compared with the existing estimators. In the support of the theoretical proposed work we have given numerical illustration.","PeriodicalId":13334,"journal":{"name":"Indian Journal of Pure & Applied Biosciences","volume":"12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved Linear Regression Using Auxillary Informations\",\"authors\":\"Shabnum Gul\",\"doi\":\"10.18782/2582-2845.8487\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The present study was taken under consideration in order to propose improved linear regression using auxiliary information’s of coefficient of regression, coefficient of skewness, coefficient of variation in order to achieve more precision in estimates than the already existing estimators. The properties associated with the proposed estimators are assessed by mean square error and bias and compared with the existing estimators. In the support of the theoretical proposed work we have given numerical illustration.\",\"PeriodicalId\":13334,\"journal\":{\"name\":\"Indian Journal of Pure & Applied Biosciences\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Indian Journal of Pure & Applied Biosciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18782/2582-2845.8487\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indian Journal of Pure & Applied Biosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18782/2582-2845.8487","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved Linear Regression Using Auxillary Informations
The present study was taken under consideration in order to propose improved linear regression using auxiliary information’s of coefficient of regression, coefficient of skewness, coefficient of variation in order to achieve more precision in estimates than the already existing estimators. The properties associated with the proposed estimators are assessed by mean square error and bias and compared with the existing estimators. In the support of the theoretical proposed work we have given numerical illustration.