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Activity Number: 409
Type: Topic Contributed
Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
Sponsor: International Indian Statistical Association
Abstract - #308419
Title: Screening Strategies for High-Dimensional Multiple Predictor, Multiple Response Data with an Application in Genomics
Author(s): Anindya Bhadra*+ and Mehdi Maadooliat and Mohsen Pourahmadi and Veera Baladandayuthapani
Companies: Purdue University and Marquette University and Texas A&M University and The University of Texas MD Anderson Cancer Center
Keywords: Gaussian Graphical Model ; Joint Variable and Covariance Selection ; Regularization ; Screening
Abstract:

We describe a joint estimation technique for the matrices of regression coefficients and the inverse covariance in a high-dimensional seemingly unrelated regression (SUR) model. Such models have recently found applicability in a variety of areas, including, but not limited to, genomics and finance. Using a variable screening approach, we demonstrate improvement in performance - both in terms of computational speed, as well as accuracy of estimation, over recently developed competing methods. Performance comparison is done in simulations, as well as on a real genomics data set.


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