[Statlist] talks on statistics

[Christina Kuenzli]

                  ETH and University of Zurich 

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

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

      April 4, 2008       15.15-17.00   LEO C 6

      Modeling Longitudinal Data with Application to 
      the Multicenter Aids Cohort (MACS) 
      Cyntha A. Struthers, University of Waterloo, Ontario, CA  

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

      April 11, 2008     15.15-16.00   LEO C 6   
  
      Markov models for accumulating mutations
      Niko Beerenwinkel, ETH 

We introduce and analyze a waiting time model for the
accumulation of genetic changes.  The continuous time
conjunctive Bayesian network is defined by a partially
ordered set of mutations and by the rate of fixation of each
mutation.  The partial order encodes constraints on the
order in which mutations can fixate in the population,
shedding light on the mutational pathways underlying the
evolutionary process. We study a censored version of the
model and derive equations for an EM algorithm to perform
maximum likelihood estimation of the model parameters.  We
also show how to select the maximum likelihood poset.  The
model is applied to genetic data obtained from tumors and to
mutations in HIV that confer drug resistant.

      April 11, 2008     16.15-17.00    LEO C 6    
      Graphical models for partially observed data-generating
      processes
      Thomas Richardson, Washington Uni, Seattle

Directed acyclic graph (DAG) models, also known as Bayesian 
networks, are often used to represent causal or data-generating
processes. There are Fisher-consistent model selection algorithms 
that will identify the generating DAG (up to equivalence) under certain
assumptions. Perhaps the strongest assumption typically made by these
procedures is that there are no unmeasured 'confounding' variables. 
In this talk I will describe recent research aimed at developing graphical
models that can accommodate the possibility of confounding variables, while
retaining many of the desirable properties of DAG models. 

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

     
________________________________________________________
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/~