Abstract
DATE: |
May 21, 2009 |
TIME: |
1:15 - 3:00 pm |
LOCATION: |
Center for Clinical Sciences Research (CCSR), Rm 4205 |
TITLE: |
Estimating sparse network models |
SPEAKER: |
Bala Rajaratnam |
The statistical modeling of networks can be useful for discovering
complex multivariate dependencies, especially for high-dimensional data
when the sample size is relatively small. Existing methods in the
literature propose methods for recovering the concentration matrix
or covariance matrix when the associate graph is sparse. In many real
life applications networks also have some nodes/variables with a high
degree of connectivity. These nodes play a critical role in the functioning
of the network. In this paper we formally define the class of sparse
network models with "highly leveraged" nodes and explore inference for
this class of models. We propose a penalized log-likelihood method for
recovering this model from data that has the same computational efficiency
as current methods in the literature. We observe that our approach achieves
its objectives by simultaneously estimating the sparse network and the
highly connected nodes correctly. The methodology is applied to real life
data sets and simulation experiments to illustrate the efficacy of our
procedure. This is work in progress that is part of an ongoing
collaboration with the medical school.
This is joint work with R.Mazumder.
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