[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

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       We are glad to announce the following talks
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       Friday, January 12, 2007, 15.15, LEO C 6

       Optimal Passion at a Distance

       Richard Gill, University of Leiden

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       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.

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        Friday, January, 26, 2007, 15,15 LEO A2
 
        Recent Development in Measurement Error Models
         
        Yanyuan Ma, Universite de Neuchatel  


  In this talk, I will illustrate some recent development in measurement
  error models, including both parametric and semiparametric
  modeling. Inference procedures will be derived and their
  optimal/suboptimal properties will be explained under various
  assumptions. Computational issues will be addressed. The new methods will
  be linked to several existing methods under special circumstances. Some
  variable selection procedures in measurement error models will be
  considered as well.

________________________________________________________
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/~