Bayesian analysis of optional unrelated question randomized response models

• Published in: Communications in statistics. Theory and methods Vol. 50; no. 18; pp. 4203 - 4215
• Main Authors: Narjis, Ghulam, Shabbir, Javid
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 20.08.2021; Taylor & Francis Ltd
• Subjects: 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
• DOI: 10.1080/03610926.2020.1713367

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.