[Statlist] Next talk: Friday, March 30, 2012 with Claudia Czado and Alexander Bauer, Technische Universität München

[Cecilia Rey]

ETH and University of Zurich

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


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We are glad to announce the following talk

Friday, March 30, 2012, 15.15h, HG G 19.1

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by Claudia Czado and Alexander Bauer, Technische Universität München


Title:
Model selection for pair-copula constructions of regular vine and non- 
Gaussian DAG models

Absract:
Pair-copula constructions (PCCs) allow to build very flexible  
multivariate statistical models based on a graphical representation  
called a regular vine (Kurowicka and Cooke, 2006) as well as models  
represented by directed acyclic graphs (DAGs). PCCs are very useful  
for modeling multivariate data in economics and finance, since they  
can capture non-symmetric and different tail dependences for different  
pairs of variables separately. Vine models are characterized by a  
sequence of linked trees called a vine-tree structure, bivariate  
copula families and families of marginal distributions. Two often  
studied subclasses are C- and D-vines. The multivariate normal and t  
distribution families are special cases. Moreover, PCCs can be used to  
construct non-Gaussian DAG models. First, research was focused on the  
development of efficient estimation methods. For regular vine models  
see for example Aas et. al. (2009) for likelihood based and Min and  
Czado (2010) for Bayesian estimation methods. For non-Gaussian DAGs  
model formulation and estimation methods are considered in Bauer et.  
al. (2012). Since the class of regular vine models is very large,  
model selection is vital. Dissmann et. al. (2011) provide a fast  
selection method in which trees are sampled sequentially using  
algorithms for weighted graphs. Bayesian alternatives are available.  
For non-Gaussian DAGs the model selection involves also a data-based  
selection of the DAG. We provide an alternative approach to the PC  
algorithm (Spirtes et. al., 2001) based on regular vines to allow for  
the detection of non-Gaussian dependency structures and compare its  
performance to the benchmark PC algorithm based on an independence  
test for zero partial correlation. We will discuss these PCC models  
and the associated selection methods as well illustrate them in an  
application to daily stock returns.

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



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