Abstract
Knowing when a process has changed would simplify the search for and identification of the special cause. Consequently, having an estimate of the process change point following a control chart signal would be useful to process engineers. In this research a maximum likelihood change point estimator is proposed for the natural location parameter of densities belonging to the exponential family. It is assumed that the behavior in the location parameter over discrete sampling intervals is adequately modeled by a linear predictor. The estimator is intended to be applied to data obtained following signals from univariate statistical process control charts in an effort to aid process engineers diagnose the root cause of process change.
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