Step Change Point Estimation in g and h Control Charts in Healthcare

Abstract

Estimating the real time of a change point in process is one of the main purposes in the statistical process control. On the other hand, it is important to estimate the change point in healthcare processes regarding the relation between quality engineering and hospital epidemiology. Hence, in this paper, maximum likelihood estimators are proposed in <em>g</em> and <em>h</em> control charts for healthcare systems. We applied Monte Carlo simulation to assess the proposed approaches in terms of accuracy and precision. In addition, there are provided the corresponding cardinality sets and coverage probabilities based on logarithm of the likelihood function. Results indicate that the proposed estimators have a satisfactory performance under different shifts.