Additive ratio type exponential estimator of finite population mean of sensitive variable using non-sensitive auxiliary information based on optional randomized response model
{"title":"Additive ratio type exponential estimator of finite population mean of sensitive variable using non-sensitive auxiliary information based on optional randomized response model","authors":"L. Grover, Amanpreet Kaur","doi":"10.1214/22-bjps535","DOIUrl":null,"url":null,"abstract":"Abstract. The appropriate use of auxiliary information in sample surveys increases the efficiency of estimator for parameter of interest. In this paper, we have proposed an exponential type estimator for the population mean of a sensitive study variable based on an optional randomized response model by using the known information on a non-sensitive auxiliary variable. Expressions for the bias and the mean square error (MSE) of the proposed estimator are derived, up to first order of approximation. For this proposed estimator, efficiency comparisons with the existing estimators have been carried out both theoretically and numerically. It has been shown that our proposed estimator perform better than the existing estimators based on the same optional randomized response model even for the small correlation between auxiliary variable and study variable. To support the results obtained,we have also studied the performance of the proposed exponential estimator using simulation technique.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brazilian Journal of Probability and Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1214/22-bjps535","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract. The appropriate use of auxiliary information in sample surveys increases the efficiency of estimator for parameter of interest. In this paper, we have proposed an exponential type estimator for the population mean of a sensitive study variable based on an optional randomized response model by using the known information on a non-sensitive auxiliary variable. Expressions for the bias and the mean square error (MSE) of the proposed estimator are derived, up to first order of approximation. For this proposed estimator, efficiency comparisons with the existing estimators have been carried out both theoretically and numerically. It has been shown that our proposed estimator perform better than the existing estimators based on the same optional randomized response model even for the small correlation between auxiliary variable and study variable. To support the results obtained,we have also studied the performance of the proposed exponential estimator using simulation technique.
期刊介绍:
The Brazilian Journal of Probability and Statistics aims to publish high quality research papers in applied probability, applied statistics, computational statistics, mathematical statistics, probability theory and stochastic processes.
More specifically, the following types of contributions will be considered:
(i) Original articles dealing with methodological developments, comparison of competing techniques or their computational aspects.
(ii) Original articles developing theoretical results.
(iii) Articles that contain novel applications of existing methodologies to practical problems. For these papers the focus is in the importance and originality of the applied problem, as well as, applications of the best available methodologies to solve it.
(iv) Survey articles containing a thorough coverage of topics of broad interest to probability and statistics. The journal will occasionally publish book reviews, invited papers and essays on the teaching of statistics.