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Activity Number:
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8
- Recent Advances in Statistical Learning for High-Dimensional and Heterogeneous Complex Data
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Type:
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Invited
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #314442
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Title:
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Large matrix estimation for time series data
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Author(s):
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Hai Shu and Bin Nan*
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Companies:
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New York University and University of California, Irvine
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Keywords:
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Abstract:
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We consider the estimation of large covariance and precision matrices from high-dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal dependence. The temporal dependence is allowed to be long-range so with longer memory than those considered in the current literature. We show that several commonly used methods for independent observations can be applied to the temporally dependent data. The rates of convergence are obtained. A gap-block cross-validation method is proposed for the tuning parameter selection, which performs well in simulations.
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Authors who are presenting talks have a * after their name.
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