Bayesian estimation in alternative tripartite randomized response techniques

In this work, a new method of Bayesian estimation for the Alternative Tripartite random-ized response technique used in obtaining the proportion of people that belongs to sen-sitive character was proposed. The proposed approach accommodates other intrinsic pa-rameter constraints in the posterior to improve statistical precision. The efficiency of the newly proposed Bayesian estimators was established for a wide interval of the values of the population proportion of the sensitive character (pi). It was discovered that for any pre-set probabilities, the developed Bayesian estimators became better in capturing responses from respondents than any other classical estimators. Therefore, as the sample size in-creases and the proposed Bayesian models capture more and more sensitive characters, the Kumaraswamy prior estimator becomes more efficient while the Generalized beta prior es-timator performs better when there are fewer people involved in the sensitive character. Applying the Bayesian methods to data on drug use disorder also confirmed their efficiency over the classical methods.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )