[Statlist] talks on statistic

[Christina Kuenzli]

              ETH and University of Zurich 

                           Proff. 
         A.D. Barbour - P. Buehlmann - F. Hampel 
              H.R. Kuensch - S. van de Geer

***********************************************************    
       We are glad to announce the following talks
***********************************************************

       Friday, January 12, 2007, 15.15, LEO C 6

       Optimal Passion at a Distance

       Richard Gill, University Leiden

I explain quantum nonlocality experiments and discuss how to 
optimize them. Statistical tools from missing data maximum 
likelihood are crucial.
New results are given on Bell, GHZ, CGLMP, CH and Hardy ladder
inequalities. Open problems are discussed.
Prior knowledge of quantum theory or indeed physics is not 
needed to follow the talk; indeed its lack could be an advantage ;-)

It will be difficult to resist discussion of the philosophical
implications of Bell's inequality.

Slides for a previous version of this talk, and reference to an
overview paper:
http://www.math.leidenuniv.nl/~gill/betterbelltalk.pdf
http://arxiv.org/abs/math.ST/0610115
[to appear in IMS Lecture Notes - Monographs series,
volume on "Asymptotics: particles, processes and inverse 
problems"]

***********************************************************
       Friday, January 19, 2007, 15.15, LEO C 6

       A robust procedure for Gaussian graphical model search from
       microarray data with p larger than n
       Alberto Roverato, Universita di Bologna


Learning of large--scale networks of interactions from microarray
data is an important and challenging problem in bioinformatics. A
widely used approach is to assume that the available data constitute
a random sample from a multivariate distribution belonging to a
Gaussian graphical model. As a consequence, the prime objects of
inference are full--order partial correlations which are partial
correlations between two variables given the remaining ones. In the
context of microarray data the number of variables exceed the sample
size and this precludes the application of traditional structure
learning procedures because a sampling version of full--order
partial correlations does not exist. In this paper we consider
limited--order partial correlations, these are partial correlations
computed on marginal distributions of manageable size, and provide a
set of rules that allow one to assess the usefulness of these
quantities to derive the independence structure of the underlying
Gaussian graphical model. Furthermore, we introduce a novel
structure learning procedure based on a quantity, obtained from
limited--order partial correlations, that we call the non--rejection
rate. The applicability and usefulness of the procedure are
demonstrated by both simulated and real data.
This is a joint work with Robert Castelo, Pompeu Fabra University,
Barcelona, Spain.

***********************************************************

________________________________________________________
Christina Kuenzli            <kuenzli at stat.math.ethz.ch>
Seminar fuer Statistik      
Leonhardstr. 27,  LEO D11      phone: +41 (0)44 632 3438         
ETH-Zentrum,                   fax  : +41 (0)44 632 1228 
CH-8092 Zurich, Switzerland        http://stat.ethz.ch/~