An efficient new scrambled response model for estimating sensitive population mean in successive sampling

In this paper, a new scrambled randomized response (SRR) model has been proposed for estimating the population mean of a sensitive variable in presence of scrambled response under simple random sampling with replacement (SRSWR). The utility of proposed SRR model under two occasions successive sampli...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 52; no. 11; pp. 5327 - 5344
Main Authors Narjis, Ghulam, Shabbir, Javid
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.11.2023
Taylor & Francis Ltd
Subjects
Bias
Estimators
Mean squared error
Privacy protection
Random sampling
Scrambled response
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Summary:In this paper, a new scrambled randomized response (SRR) model has been proposed for estimating the population mean of a sensitive variable in presence of scrambled response under simple random sampling with replacement (SRSWR). The utility of proposed SRR model under two occasions successive sampling is also explored. It is found that the proposed SRR model is superior to the additive model of Gjestvang and Singh ( 2009 ) under SRSWR and successive sampling. We also proposed a composite class of estimators for estimating the population mean of sensitive variable under two occasions successive sampling. The proposed composite class of estimators under optimum conditions is shown to be more efficient than the classical ratio and exponential ratio type estimators respectively.
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content type line 14
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2021.1986528