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