Improved randomized response technique for estimating population proportion of a sensitive characteristic

Q4 Mathematics

Model Assisted Statistics and Applications Pub Date : 2022-05-23 DOI:10.3233/mas-220012

Manpreet Kaur, I. S. Grewal, S. S. Sidhu

{"title":"Improved randomized response technique for estimating population proportion of a sensitive characteristic","authors":"Manpreet Kaur, I. S. Grewal, S. S. Sidhu","doi":"10.3233/mas-220012","DOIUrl":null,"url":null,"abstract":"Getting correct answers to sensitive questions from the respondents and estimating the population parameters on variables that are sensitive in nature is prevailing problem in survey sampling. In the present research paper, the problem of estimation of the population proportion of sensitive characteristics has been studied. For this, an improved randomized response device has been developed by taking the two cases of the unrelated question, case-I: ‘when the proportion of unrelated characteristic is known’ and other case-II: ‘when the proportion of unrelated characteristic is not known’. Two estimators of the population proportion of a sensitive characteristic have been proposed, one for a known value of unrelated characteristic πy and the other for an unknown value, which were found to be unbiased. The expression for variances and unbiased estimates for the variances of the proposed estimators have been obtained. The optimum value of sample sizes has been worked out for which the minimum variance for the proposed estimators has also been obtained. An empirical study has been conducted and concluded graphically that proposed estimators are better than the estimators of Mangat (1992) and Tiwari and Mehta (2016).","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Model Assisted Statistics and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/mas-220012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Mathematics","Score":null,"Total":0}

引用次数: 0

Abstract

Getting correct answers to sensitive questions from the respondents and estimating the population parameters on variables that are sensitive in nature is prevailing problem in survey sampling. In the present research paper, the problem of estimation of the population proportion of sensitive characteristics has been studied. For this, an improved randomized response device has been developed by taking the two cases of the unrelated question, case-I: ‘when the proportion of unrelated characteristic is known’ and other case-II: ‘when the proportion of unrelated characteristic is not known’. Two estimators of the population proportion of a sensitive characteristic have been proposed, one for a known value of unrelated characteristic πy and the other for an unknown value, which were found to be unbiased. The expression for variances and unbiased estimates for the variances of the proposed estimators have been obtained. The optimum value of sample sizes has been worked out for which the minimum variance for the proposed estimators has also been obtained. An empirical study has been conducted and concluded graphically that proposed estimators are better than the estimators of Mangat (1992) and Tiwari and Mehta (2016).

查看原文本刊更多论文

估计敏感特征总体比例的改进随机响应技术

从受访者那里获得敏感问题的正确答案，并根据本质上敏感的变量估计人口参数，是调查抽样中的主要问题。在本研究论文中，研究了敏感特征的人口比例估计问题。为此，通过对不相关问题的两种情况进行研究，开发了一种改进的随机反应装置，案例一：“当不相关特征的比例已知时”，另一个案例二：“当无关特征的比例未知时”。提出了两种敏感特征总体比例的估计量，一种是不相关特征πy的已知值，另一种是未知值，发现它们是无偏的。得到了估计量的方差表达式和方差的无偏估计。给出了样本大小的最优值，并得到了所提出的估计量的最小方差。已经进行了一项实证研究，并以图表形式得出结论，所提出的估计量优于Mangat（1992）、Tiwari和Mehta（2016）的估计量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Model Assisted Statistics and Applications Mathematics-Applied Mathematics

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.