Activity Number:
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205
- New Direction for Model Selection in Big Data
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Type:
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Invited
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Date/Time:
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Monday, July 31, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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Abstract #322460
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Title:
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Variable Selection for Bilinear Regression with Matrix Covariates
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Author(s):
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Dan Yang*
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Companies:
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Rutgers University
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Keywords:
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Abstract:
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Rapid development of modern technology has generated numerous high-dimensional datasets with matrix-valued covariates. Application of traditional LASSO after vectorization of these matrix-valued covariates leads to inferior estimation and selection results. An existing proposal that preserves the matrix structure through bilinear regression adopts a penalty that essentially assumes the same degree of sparsity along the rows and columns, which is often violated by real data. We introduce a novel penalty that solves this problem. The resulting method is minimax rate optimal and outperforms the existing one in the simulation studies.
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Authors who are presenting talks have a * after their name.
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