Shewhart ridge profiling for the Gamma response model

Abstract

ABSTRACT When product quality follows the Gamma distribution and is related to one or more covariate(s), then Gamma regression model (GRM) profiling will be used. The Gamma profiling is generally based on a maximum likelihood estimator (MLE). In GRM profiling, when two or more covariates are linearly related, the MLE-based GRM profiling is unsuitable. In this situation, an alternative to MLE-based profiling is required. So, this study develops the Gamma ridge regression profiling based on Pearson and deviance residuals. The performance of the proposed profiling is evaluated with the help of a simulation study under different conditions, where average run length is considered as the performance evaluation criterion. The simulation findings show that the Pearson residual based profiling with ridge estimator is better than the deviance residuals-based profiling with MLE as well as ridge estimator. Furthermore, we consider an application to evaluate the performance of the proposed methods.