Modeling and analysis of dynamic robust design experimentsThe GLRT for statistical process control of autocorrelated processes

摘要

This paper investigates the response model approach for the dynamic robust design problem. We derive relationships between the effect estimates of the loss model approach and those of the response model approach. We show that the bias problem for the static case solely exists in the estimation of process variance and does not exist in the estimation of the slope and intercept parameters. The two analysis approaches are compared by use of a real example.This paper presents an on-line Statistical Process Control (SPOC) technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in autocorrelated processes that follow a normally distributed Autoregressive Integrated Moving Average (ARIMA) model. The GLRT is applied to the uncorrelated residuals of the appropriate time-series model. The Performance of the GLRT is compared to two other commonly applied residual-based tests - a Shewhart individuals chart and a CUSUM test. A wide range of ARIMA models are considered, with the conclusion that the best residual-based test to use depends On the particular ARIMA model used to describe the autocorrelation.