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Activity Number: 5
Type: Invited
Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #310511 View Presentation
Title: Distance Correlation Applied to High-Dimensional Astrophysical Databases
Author(s): Mercedes T. Richards*+ and Elizabeth Martinez-Gomez and Donald Richards
Companies: Penn State and Instituto Tecnológico Autónomo de México and Penn State
Keywords: galaxies ; cosmology ; surveys ; clusters
Abstract:

Statistical studies of associations in high-dimensional astrophysical databases have generally been based on the Pearson correlation coefficient, the classical measure of linear relationships between two variables, although it is known that some astrophysical variables have nonlinear relationships. Since the distance correlation measure is applicable to random variables of any dimension and can detect nonlinear associations, we applied this measure to the COMBO-17 high-dimensional, large-sample astrophysical database of over 50,000 galaxies from the Chandra Deep Field South region. We compared the distance correlation for pairs of variables with the corresponding Pearson correlation coefficient and also with corresponding values of another measure called the maximal information coefficient. We also examined the level of association between individual pairs of variables. For given values of the Pearson coefficient, distance correlation is more effective in identifying potential outliers, and it has a greater ability than the maximal information coefficient to resolve astrophysical data into highly concentrated V-shapes, which enhances classification and pattern identification.


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