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Activity Number:
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102
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Quality and Productivity
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| Abstract - #301712 |
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Title:
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Diagnostics After a Signal from Control Charts in a Normal Process
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Author(s):
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Jianying Lou*+ and Marion R. Reynolds, Jr.+ and Dong-Yun Kim
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Companies:
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Virginia Polytechnic Institute and State University and Virginia Polytechnic Institute and State University and Virginia Polytechnic Institute and State University
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Address:
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Department of Statistics, Blacksburg, VA, 24061, Department of Statistics, Blacksburg, VA, 24061,
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Keywords:
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Control chart ; signal ; diagnostics ; maximum likelihood
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Abstract:
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Control charts are fundamental SPC tools for process monitoring. When a combination of charts signals, knowing the change point, which distributional parameter changed, and/or the change size helps to identify the cause of the change. Traditionally the change point and the changed parameter are determined by informally examining the control charts. Maximum likelihood (ML) estimation of the change point is also used in SPC, but estimating the current process mean associated with special causes has mainly been done in EPC. In this paper, we develop diagnostics to provide information about the change point, the changed parameter and the change size. We propose using ML estimators of the current process parameters and their confidence intervals to identify and estimate the changed parameters. This approach works well in most cases, and has better performance than the traditional approach.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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