Abstract Details
Activity Number:
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275
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #311547
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Title:
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Convex Banding of the Covariance Matrix
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Author(s):
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Jacob Bien*+ and Florentina Bunea and Luo Xiao
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Companies:
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Cornell University and Cornell University and Johns Hopkins University
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Keywords:
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covariance ;
high-dimensional ;
group lasso
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Abstract:
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We introduce a sparse and positive definite estimator of the covariance matrix designed for high-dimensional situations in which the variables have a known ordering. Our estimator is the solution to a convex optimization problem that involves a hierarchical group lasso penalty. We show how it can be efficiently computed, compare it to other methods such as tapering by a fixed matrix, and develop several theoretical results that demonstrate its strong statistical properties. Finally, we show how using convex banding can improve the performance of high-dimensional procedures such as linear and quadratic discriminant analysis.
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Authors who are presenting talks have a * after their name.
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