[Statlist] Next talk: Friday, November 11, 2011 with Florian Frmmlet

[Cecilia Rey]

ETH and University of Zurich

Proff. P. Buehlmann -  L. Held - H.R. Kuensch -
M. Maathuis -  S. van de Geer - M. Wolf


***********************************************************
We are glad to announce the following talk
Friday, November 11, 2011, 15.15h, HG G 19.1

**********************************************************
  by Florian Frommlet, Universität Wien

  Titel:
Modifications of BIC for model selection under sparsity: Theory and  
applications in genetics

Abstract:
In many research areas today the number of features p for which data  
is collected is much larger than the sample size n based on which  
inference is made. This is especially true for genetical applications  
like QTL mapping or genome wide association studies (GWAS). Sparsity  
is a key notion to be able to perform statistical analysis when p >>  
n. It means that the number of true signals is small compared with the  
sample size. This talk will focus on certain modifications of  
Schwarz's Bayesian information criterion (mBIC and mBIC2) which have  
been developed to perform model selection under sparsity. These  
selection criteria are designed in such a way that in case of  
orthogonal regressors mBIC controls the family wise error rate, while  
mBIC2 controls the false discovery rate.
After introducing the notion of asymptotic Bayes optimality under  
sparsity (ABOS) we will present recent results concerning some  
classical multiple testing procedures: While the Bonferroni procedure  
is ABOS only in case of extreme sparsity, it turns out that the  
Benjamini Hochberg procedure nicely adapts to the unknown level of  
sparsity. These results can be translated for mBIC and mBIC2 in the  
context of model selection. While the theory has been developed so far  
only for the case of orthogonal designs, simulation studies indicate  
that good properties of mBIC and mBIC2 also hold in more general  
situations. We will discuss the case of densely spaced markers in QTL  
mapping with experimental populations, where specific theory has been  
developed how to consider the correlation structure of markers.  
Finally we will present results from a comprehensive simulation study  
based on real SNP data, which illustrate the relevance of our approach  
to analyze GWAS data.

The abstract is also to be found here:  http://stat.ethz.ch/events/research_seminar



	[[alternative HTML version deleted]]