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.”
Register or login for access to this item and much more
All Information Management content is archived after seven days.
Community members receive:
- All recent and archived articles
- Conference offers and updates
- A full menu of enewsletter options
- Web seminars, white papers, ebooks
Already have an account? Log In
Don't have an account? Register for Free Unlimited Access