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Towards Improved Analysis Methods for Two‐Level Factorial Experiments with Time Series Responses
Author(s) -
Vanhatalo Erik,
Bergquist Bjarne,
Vännman Kerstin
Publication year - 2013
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1424
Subject(s) - spurious relationship , series (stratigraphy) , noise (video) , computer science , process (computing) , statistics , factorial experiment , time series , response time , factorial , econometrics , mathematics , artificial intelligence , paleontology , mathematical analysis , biology , computer graphics (images) , image (mathematics) , operating system
Dynamic processes exhibit a time delay between the disturbances and the resulting process response. Therefore, one has to acknowledge process dynamics, such as transition times, when planning and analyzing experiments in dynamic processes. In this article, we explore, discuss, and compare different methods to estimate location effects for two‐level factorial experiments where the responses are represented by time series. Particularly, we outline the use of intervention‐noise modeling to estimate the effects and to compare this method by using the averages of the response observations in each run as the single response. The comparisons are made by simulated experiments using a dynamic continuous process model. The results show that the effect estimates for the different analysis methods are similar. Using the average of the response in each run, but removing the transition time, is found to be a competitive, robust, and straightforward method, whereas intervention‐noise models are found to be more comprehensive, render slightly fewer spurious effects, find more of the active effects for unreplicated experiments and provide the possibility to model effect dynamics. Copyright © 2012 John Wiley & Sons, Ltd.