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Outlier analysis: Chebyschev criteria vs approach based on Mutual Information for quant analytics

(Last Updated On: May 25, 2012)

Outlier analysis: Chebyschev criteria vs approach based on Mutual Information
Text & Data Mining by practical means textanddatamining.blogspot.com

As often happens, I usually do many thing in the same time, so during a break while I was working for a new post on applications of mutual information in data mining, I read the interesting paper suggested by Sandro Saitta on…

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My experience over many, many years with outliers is that almost by definition they exist only because the evaluating measure, that is, the “questions” posed or the “hypotheses” tested or the “tags” applied had little to nearly nothing to do with the real state of the system at the outlier position. For example, it is like discovering a tree in a flower garden. Flower assessment criteria are confounded by the tree. sic sum quo erata!!
Elyas F. Isaacs, Ph.D. in the City of New York

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