Home > Events > 2013 Seminars & Colloquia > WEI PAN - Division of Biostatistics, University of Minnesota

WEI PAN - Division of Biostatistics, University of Minnesota

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” A network-based penalized regression method with application to genomic data”
When
18 April 2013 from 4:00 PM to 5:00 PM
Where
201 Thomas Bldg.
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Penalized regression approaches are attractive in dealing with high-dimensional data such as arising in high-throughput genomic studies. New methods have been introduced to utilize the network structure of predictors, e.g. gene networks, to improve parameter estimation and variable selection (Li and Li 2008, 2010; Pan 2009; Pan {\it et al.} 2010). All the existing network-based penalized methods are based on an assumption that parameters, e.g. regression coefficients, of neighboring nodes in a network are close in magnitude, which however may not hold.

In this paper we propose a novel penalized regression method based on a weaker prior assumption that the parameters of neighboring nodes in a network are likely to be zero (or non-zero) at the same time, regardless of their specific magnitudes. We propose a novel non-convex penalty function to incorporate this prior, and an algorithm based on difference convex programming.  We use simulated data and a gene expression dataset
to demonstrate the advantages of the proposed method over some existing methods.

This is based on joint work with Sunny Kim and Xiaotong Shen.


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