Lakhan Singh, Diksha Malik, Manish Kumar, S. K. Yadav
{"title":"Refined batch of Estimators for Estimating Population Mean with the help of known Auxiliary Parameters","authors":"Lakhan Singh, Diksha Malik, Manish Kumar, S. K. Yadav","doi":"10.59467/ijass.2024.20.299","DOIUrl":null,"url":null,"abstract":"In the presented work, we have instituted a batch of ratio type estimators for estimating the population mean under simple random sampling with the help of known auxiliary variables or function of auxiliary variables, for more refined result. The expression of bias and mean square error of the instituted class are induced up to the first order of approximation. The strength of the proposed batch of estimators is compared with some existing estimators in terms of MSEs, first theoretically and then a numerical study is also carried out by using real data set to support the findings. It is concluded that the proposed class outperforms the various prevailing estimators in terms of minimum MSE and higher percentage relative efficiency.. KEYWORDS :Estimator, Mean, Bias, Mean square error, Percentage relative efficiency, Auxiliary information.","PeriodicalId":50344,"journal":{"name":"International Journal of Agricultural and Statistical Sciences","volume":"89 1","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Agricultural and Statistical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.59467/ijass.2024.20.299","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In the presented work, we have instituted a batch of ratio type estimators for estimating the population mean under simple random sampling with the help of known auxiliary variables or function of auxiliary variables, for more refined result. The expression of bias and mean square error of the instituted class are induced up to the first order of approximation. The strength of the proposed batch of estimators is compared with some existing estimators in terms of MSEs, first theoretically and then a numerical study is also carried out by using real data set to support the findings. It is concluded that the proposed class outperforms the various prevailing estimators in terms of minimum MSE and higher percentage relative efficiency.. KEYWORDS :Estimator, Mean, Bias, Mean square error, Percentage relative efficiency, Auxiliary information.