An Adjusted General Family of Population Mean Estimators in the Presence of Non-response under Two-phase Sampling without Known Population Mean of Auxiliary Variable
{"title":"An Adjusted General Family of Population Mean Estimators in the Presence of Non-response under Two-phase Sampling without Known Population Mean of Auxiliary Variable","authors":"Nuanpan Lawson, Thanapanang Rachokarn","doi":"10.1145/3328886.3328887","DOIUrl":null,"url":null,"abstract":"In this paper, a new family of estimators to estimate population means of a study variable has been proposed under two situations; non-response occurrence in a study variable only and non-response occurrence in both the study and auxiliary variables under two-phase sampling. We assumed that the population mean of an auxiliary variable is unknown. We derive the bias and mean square error of the proposed estimators up to a first order approximation. An empirical study of the proposed estimators shows that they perform better than other existing estimators in terms of a percentage relative efficiency.","PeriodicalId":194074,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computers in Management and Business","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 2nd International Conference on Computers in Management and Business","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3328886.3328887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a new family of estimators to estimate population means of a study variable has been proposed under two situations; non-response occurrence in a study variable only and non-response occurrence in both the study and auxiliary variables under two-phase sampling. We assumed that the population mean of an auxiliary variable is unknown. We derive the bias and mean square error of the proposed estimators up to a first order approximation. An empirical study of the proposed estimators shows that they perform better than other existing estimators in terms of a percentage relative efficiency.