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Articles
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QICID: 30609
Title: A Multistep, Cluster-Based Multivariate Chart for Retrospective Monitoring of Individuals
Copyright: ASQ
Author: Jobe, Marcus J.; Pokejovy, Michael
Organization: Miami University; Universitat Konstanz
Subject: Cluster analysis; Kernel density estimates; Moving average chart; Outliers; Density estimation;
Series: Journal of Quality Technology, Vol. 41, No. 4, October 2009, pp. 323-339
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Abstract: [This abstract is based on the authors' abstract.]
The Hotelling’s T² approach for outlier detection in an individuals retrospective multivariate control chart gives unreliable results because the presence of outliers distorts both the sample mean vector and the covariance matrix. To remedy this problem, a computer-intensive, multistep cluster-based method is proposed to overcome the distortion or masking. Simulation studies show the procedure is better than classical or robust procedures at detecting randomly occurring outliers and outliers resulting from shifts in the process location. Comparisons based on real data are provided.
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