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Towards improved analysis methods for two-level factorial experiments with time series responses
Quality Technology and Management, Luleå University of Technology, Luleå, Sweden.
Quality Technology and Management, Luleå University of Technology, Luleå, Sweden.
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Department of Engineering Sciences and Mathematics, Luleå University of Technology, Luleå, Sweden and Department of Engineering Science, University West, Trollhättan, Sweden.
2013 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 29, no 5, 725-741 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2013. Vol. 29, no 5, 725-741 p.
Keyword [en]
two-level factorial design, time series analysis, intervention-noise model, location effects, simulation
National Category
Probability Theory and Statistics
URN: urn:nbn:se:umu:diva-59848DOI: 10.1002/qre.1424OAI: diva2:556942
Available from: 2012-09-26 Created: 2012-09-26 Last updated: 2013-10-13Bibliographically approved

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