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HBOK 1-406

Title Statistics Roundtable: Detection Decisions
Author(s) Robert L. Mason and John C. Young
Source Quality Progress
Topic Data Analysis
Abstract Outliers in an industrial process become more and more difficult to detect with an increase in the dimensionality of the data. Although an outlier may not stick out on the end of the data distribution for multiple variables, they will stick out somewhere. Some of the more recent procedures for detecting multivariate outliers include those based on the use of robust estimators. Such outlier detection schemes are not subject to the masking and swamping problems that can plague methods based on common estimators. In addition, outliers do not have the same ill effects on the robust estimators as they do on the common estimators. Robust estimates of the variances, means and correlations of the related variables, however, may be far removed from the true values of these statistics. In addition, many of these procedures are limited from practical use because they can be computationally intense. Choose the right statistical procedures to examine data and find outliers.
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Link for .PDF http://www.asq.org/quality-progress/2010/04/statistics-roundtable/detection-decisions.pdf
Link for HTML http://www.asq.org/quality-progress/2010/04/statistics-roundtable/detection-decisions.html
Reference Code 1-406

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