While recently conducting my periodic review of the Social Science Statistics Blog from Harvard’s Institute for Quantitative Social Science, I came across several intriguing presentations, including one entitled “Detecting Novel Bivariate Relationships in Large Data Sets” by Ph.D./M.D. student David Reshef.

In the brief abstract, Reshef introduces the maximal information coefficient (MIC) as a measure of covariation between two variables that captures “a wide range of associations … and assigns similar scores to relationships with similar noise levels.” He adds that “MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships.”

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