{"title":"Generalized Class of Finite Population Variance in the Presence of Random Nonresponse Using Simulation Approach","authors":"Sapna Javed, S. Masood, A. Shokri","doi":"10.1155/2023/6643435","DOIUrl":null,"url":null,"abstract":"In this article, we estimate the finite population variance in random nonresponse using simple random sampling, which may be helpful for data analysis in applied and environmental sciences. For the three distinct random nonresponse techniques by Singh and Joarder [25], we have proposed a generalized class of exponential-type estimators that uses an auxiliary variable. Up to the first order of approximation, expressions of the bias and mean square error of the proposed estimators are obtained. The suggested estimators illustrate their superior performances to the current estimators in the comparable strategies in a comparative analysis using the real and simulated datasets.","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":"7 1","pages":"6643435:1-6643435:16"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complex psychiatry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2023/6643435","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 article, we estimate the finite population variance in random nonresponse using simple random sampling, which may be helpful for data analysis in applied and environmental sciences. For the three distinct random nonresponse techniques by Singh and Joarder [25], we have proposed a generalized class of exponential-type estimators that uses an auxiliary variable. Up to the first order of approximation, expressions of the bias and mean square error of the proposed estimators are obtained. The suggested estimators illustrate their superior performances to the current estimators in the comparable strategies in a comparative analysis using the real and simulated datasets.