Bayesian analysis of optional unrelated question randomized response models

The randomized response technique (RRT) is an effective method designed to obtain the sensitive information from respondents while assuring the privacy. Narjis and Shabbir [Narjis, G., and J. Shabbir. 2018. Estimation of population proportion and sensitivity level using optional unrelated question r...

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Veröffentlicht in:Communications in statistics. Theory and methods Jg. 50; H. 18; S. 4203 - 4215
Hauptverfasser: Narjis, Ghulam, Shabbir, Javid
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Taylor & Francis 20.08.2021
Taylor & Francis Ltd
Schlagworte:
Bayesian analysis
Beta prior
Estimates
mean squared error
optional unrelated question
Parameter estimation
Population statistics
posterior distribution
Questions
Randomized response
Sensitivity
ISSN:0361-0926, 1532-415X
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Zusammenfassung:The randomized response technique (RRT) is an effective method designed to obtain the sensitive information from respondents while assuring the privacy. Narjis and Shabbir [Narjis, G., and J. Shabbir. 2018. Estimation of population proportion and sensitivity level using optional unrelated question randomized response techniques. Communications in Statistics - Simulation and Computation 0 (0):1-15] proposed three binary optional unrelated question RRT models for estimating the proportion of population that possess a sensitive characteristic and the sensitivity level of the question. In this study, we have developed the Bayes estimators of two parameters for optional unrelated question RRT model along with their corresponding minimal Bayes posterior expected losses (BPEL) under squared error loss function (SELF) using beta prior. Relative losses, mean squared error (MSE) and absolute bias are also examined to compare the performances of the Bayes estimates with those of the classical estimates obtained by Narjis and Shabbir ( 2018 ). A real survey data are provided for practical utilizations.
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ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2020.1713367