[gov3009-l] Applied Statistics Workshop: David Reshef on Wed., March 21

Konstantin Kashin kkashin at fas.harvard.edu
Wed Mar 21 16:37:50 EDT 2012


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: http://www.exploredata.net/Downloads/MINE-Application

-Konstantin



On Mon, Mar 19, 2012 at 11:03 AM, Konstantin Kashin <kkashin at fas.harvard.edu
> wrote:

> 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=cRCIlh2G7AjiA&keytype=ref&siteid=sci>
> .
>
> 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 at fas.harvard.edu
> Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>
>
>


-- 
Konstantin Kashin
Ph.D. Student in Government
Harvard University

Mobile: 978-844-0538
E-mail: kkashin at fas.harvard.edu
Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>
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