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A model‐based approach to quality control of paper production
Author(s) -
Brown Patrick E.,
Diggle Peter J.,
Henderson Robin
Publication year - 2004
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.526
Subject(s) - multivariate statistics , constant (computer programming) , sampling (signal processing) , statistics , production (economics) , matrix (chemical analysis) , control chart , computer science , control (management) , mathematics , econometrics , artificial intelligence , materials science , filter (signal processing) , process (computing) , economics , composite material , computer vision , macroeconomics , programming language , operating system
Abstract 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. Copyright © 2004 John Wiley & Sons, Ltd.