Hello,
To follow up on Dave's excellent talk earlier today, I wanted to highlight
that the Java implementation of MINE along with wrappers for R + Python are
available here:
-Konstantin
On Mon, Mar 19, 2012 at 11:03 AM, Konstantin Kashin <kkashin(a)fas.harvard.edu
Dear all,
Please join us for the Applied Statistics Workshop (Gov 3009) this
Wednesday, March 21 from 12.00 - 1.30 pm in CGIS Knafel Room 354. David
Reshef <http://web.mit.edu/dnreshef/www/>, an MD/PhD student at the
Harvard-MIT Division of Health Sciences and Technology (HST), will give a
presentation entitled "Detecting Novel Bivariate Associations in Large Data
Sets". As always, a light lunch will be provided.
For those interested, here is the project
website<http://www.exploredata.net/>and
the accompanying
*Science*
article<http://www.sciencemag.org/content/334/6062/1518.abstract?ijkey=c…
.
Abstract:
Identifying interesting relationships between
pairs of variables in large
data sets is increasingly important. One way of doing so is to search such
data sets for pairs of variables that are closely associated. This can be
done by calculating some measure of dependence for each pair, ranking the
pairs by their scores, and examining the top-scoring pairs. We outline two
heuristic properties--generality and equitability--that the statistic we
use to measure dependence should have in order for such a strategy to be
effective.
We present a measure of dependence for two-variable relationships, the
maximal information coefficient (MIC), that has these properties. MIC
captures a wide range of associations both functional and not (generality),
and assigns similar scores to relationships with similar noise levels,
regardless of relationship type (equitability). Finally, we show that MIC
belongs to a larger class of maximal information-based nonparametric
exploration (MINE) statistics for identifying and classifying relationships.
An up-to-date schedule for the workshop is available at
http://www.iq.harvard.edu/events/node/1208.
Best,
Konstantin
--
Konstantin Kashin
Ph.D. Student in Government
Harvard University
Mobile: 978-844-0538
E-mail: kkashin(a)fas.harvard.edu
Site:
http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashi…
--
Konstantin Kashin
Ph.D. Student in Government
Harvard University
Mobile: 978-844-0538
E-mail: kkashin(a)fas.harvard.edu
Site: