Abstract Details
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Activity Number:
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373
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Quality and Productivity Section
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| Abstract - #307934 |
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Title:
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A Distribution-Free Procedure for Removing Multivariate Outliers
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Author(s):
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Robert Mason*+ and Youn-Min Chou and John C Young
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Companies:
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Southwest Research Institute and The University of Texas at San Antonio and Retired
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Keywords:
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Kernel Smoothing ;
Multivariate Statistical Process Control ;
Outliers
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Abstract:
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Outliers are observations that are discordant from the majority of the sample observations. The Hotelling's T-square statistic has been used to detect potential outliers in a Phase I operation, but its performance is affected when the underlying distribution of the quality characteristics is not following a multivariate normal distribution. Hence there is a need to develop a distribution-free procedure which applies to any underlying distribution. In this paper, a distribution-free procedure based on sequential testing for outliers and kernel distribution fitting of the T-square statistic is proposed. An example on data from an industrial process using this procedure is presented.
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