Deviance and Pearson Residuals-Based Control Charts with Different Link Functions for Monitoring Logistic Regression Profiles: An Application to COVID-19 Data

In statistical process control, the control charts are an effective tool to monitor process. When is examined based on exponential family distributed response variable along with a single explanatory variable, generalized linear model (GLM) provides better estimates and GLM-based preferred. This study designed propose using different link functions (i.e., logit, probit, c-log-log, cauchit) binary variable. The Pearson residuals (PR)- deviance (DR)-based for logistic regression proposed under functions. For evaluation purposes, simulation evaluate performance of charts. results compared average run length (ARL). Moreover, implemented real application COVID-19 death monitoring. Monte Carlo applications show that model-based c-log-log function gives as other