A novel efficient randomized response model designed for attributes of utmost sensitivity.

The existence of potential incomplete truthful reporting emerges when dealing with topics of great sensitivity. This paper introduces a novel, efficient randomized response model to address the challenges of untruthful reporting. It is meant to improve the accuracy of estimating highly sensitive attributes.

A new improved randomized response model with application to compulsory motor insurance.

One of the challenges, when investigating sensitive attributes, or information that people tend not to disclose, through surveys is the ethical obligation to preserve the privacy of respondents. Although the randomized response method, originally suggested by Warner, allows estimating the proportion of such attributes within the population while maintaining confidentiality, the variance of the estimate consistently increases if the likelihood of selecting the question about sensitive attribute increases. The purpose of this research is to introduce a new three-stage RR model which provides an efficient alternative to Warner's model that allows more credibility from a practical perspective and apply the model to estimate noncompliance ratio in compulsory motor insurance.

A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak.

We propose a new strategy for analyzing the evolution of random phenomena over time and space simultaneously based on the high-order multivariate Markov chains. We develop a novel Markov model of order for chains consisting of possible states to gather parsimony with realism. It can capture negative and positive associations among the chains with only a reduced number of parameters, , remarkably lower than required for the full parameterized model.