Likelihood and Bayesian Methods for Accurate Identification of Measurement Biases in Pseudo Steady-State Processes

Measurement Biases in Pseudo Steady-State Processes Sriram Devanathan, Stephen B. Vardeman and Derrick K. Rollins, Sr. Department of Chemical Engineering, Iowa State University, Ames, Iowa 50011 Departments of Statistics, Ames, Iowa 50011 Department of Industrial and Manufacturing Systems and Engineering, Ames, Iowa 50011 *Author to whom correspondence should be sent Abstract Two new approaches are presented for improved identification of measurement biases in linear pseudo steady-state processes. Both are designed to detect a change in the mean of a measured variable leading to an inference regarding the presence of a biased measurement. The first method is based on a likelihood ratio test for the presence of a mean shift. The second is based on a Bayesian decision rule (relying on prior distributions for unknown parameters) for the detection of a mean shift. The performance of these two methods is compared with that of a method given by Devanathan et al.. For the process studied, both techniques were found to have higher identification power than the method of Devanathan et al. and appears to have excellent but sightly lower type I error performace than the Devanathan et al. method.