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Activity Number:
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170
- Theory and Methods for High-Dimensional Data
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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Abstract #330009
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Presentation
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Title:
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A Concentration Inequality for Large Autocovariance Matrices
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Author(s):
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Yicheng Li* and Fang Han
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Companies:
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University of Washington and University of Washington
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Keywords:
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concentration inequality;
high dimensional time series;
VAR(d);
vector-valued ARCH
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Abstract:
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This paper establishes a new concentration inequality for large autocovariance matrices constructed from high dimensional structural time series that extends the inequality for product measures of Rudelson. The method is based on the Cantor-set blocking argument put forward by Merlevede et al. (2011) and Banna et al. (2016) in case of geometrically strongly mixing scalar-valued or absolutely regular matrix-valued sequences. Applications include linear VAR(d) and vector-valued ARCH models.
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Authors who are presenting talks have a * after their name.
