The existing algorithm employing the log-normal distribution lacks applicability in generating imprecise data. This paper addresses this limitation by first introducing the log-normal distribution as a means to handle imprecise data. Subsequently, we leverage the neutrosophic log-normal distribution to devise an algorithm specifically tailored for…

The various empirical models built for enrollment management, operations, and program evaluation purposes may have lost their predictive power as a result of the recent collective impact of COVID restrictions, widespread social upheaval, and the shift in educational preferences. This statistical artifact is known as model drifting, data-shift,…

Although immediacy is one of the necessary criteria to show strong evidence of a causal relation in SCDs, no inferential statistical tool is currently used to demonstrate it. We propose a Bayesian unknown change-point model to investigate and quantify immediacy in SCD analysis. Unlike visual analysis that considers only 3-5 observations in…