Article ID: | iaor2009555 |
Country: | United States |
Volume: | 30 |
Issue: | 3 |
Start Page Number: | 825 |
End Page Number: | 847 |
Publication Date: | Jun 1999 |
Journal: | Decision Sciences |
Authors: | Booth David E., Hu Michael Y., Wright Christine M. |
Keywords: | statistics: inference |
The effectiveness of the joint estimation outlier detection method as a process control technique for short autocorrelated time series is investigated and compared with exponentially weighted moving average. The research goal is to determine the effectiveness of the method for detecting out-of-control observations when they are the last observation in a short autocorrelated time series. This is an important problem because detecting an outlier in the period when it occurs, rather than several periods after it occurs, will preclude the production of more defective units. Two cases are investigated: short simulated time series when normality is assumed, and short real time series when the assumption is violated.