A model-based approach to quality control of paper production

This paper uses estimated model parameters as inputs into multivariate quality control charts. The thickness of paper leaving a paper mill is measured at a high sampling rate, and these data are grouped into successive data segments. A stochastic model for paper is fitted to each data segment, leading to parameter estimates and information-based standard errors for these estimates. The estimated model parameters vary by more than one can be explained by the information-based standard errors, suggesting that the 'true' underlying parameters are not constant over time. A model is formulated for the true parameters in which the information matrix dictates the distribution for the observed parameters given the true parameters.