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Abstract Details
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Activity Number:
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341
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #303386 |
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Title:
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A Universal Correlation Coefficient
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Author(s):
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Nuo Xu*+
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Companies:
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University of Alabama at Birmingham
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Address:
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, , ,
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Keywords:
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correlation coefficient ;
form of dependency ;
degree of dependency
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Abstract:
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Developed by Galton and Pearson more than a century ago, Correlation Coefficient is still one of the most widely known and used indexes in statistical analysis. It also spawned a number of variants, each providing either a remedy or an augmentation. However, a common limitation among them is their incapability of differentiating the effect of form of dependency from that of randomness. A universal correlation coefficient is developed based on the deviation of the average first difference on the ranked data from its expected value. Such an index can detect degree of randomness irrespective of form of dependency and provides as a commensurate correlation coefficient among pairs of variables. A comparative study on a simulated dataset is presented to show its generality over other correlation coefficients.
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Authors who are presenting talks have a * after their name.
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