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Abstract
In this paper, we extend the exponentially weighted moving average (EWMA) technique to double exponentially weighted moving average (DEWMA) technique. We show that DEWMA mean charts perform better than EWMA mean charts in detecting small mean shifts ranging from 0.1 to 0.5 of the process standard deviation, and that the two types of charts perform similarly when mean shifts are larger than 0.5 standard deviation. The design of DEWMA mean charts is also discussed.
Additional information
Notes on contributors
Lingyun Zhang
Lingyun Zhang He is a Lecturer in the Institute of Information Sciences and Technology at Massey University, New Zealand. He earned his PhD in Mathematical Statistics from the University of Regina, Canada. His research interests include SPC, applied probability and simulation. He has published papers in Journal of Quality Technology, Communications in Statistics, and the Mathematical Scientist.
Gemai Chen
Gemai Chen He is a Professor of Statistics in the Department of Mathematics and Statistics, University of Calgary. His research interests include parametric and nonparametric regression, sample survey, goodness-of-fit, survival analysis, industrial statistics, quality control and improvement, nonlinear time series modeling of environmental changes, and statistical consulting.