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Activity Number:
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147
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Type:
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Invited
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Date/Time:
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Monday, August 3, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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| Abstract - #302960 |
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Title:
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High-Dimensional Inference: From Sparse Signals Recovery to Covariance Matrix Estimation
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Author(s):
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Tony Cai*+
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Companies:
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University of Pennsylvania
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Address:
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3730 Walnut St., Philadelphia, PA, 19104,
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Keywords:
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convariance matrix estimation ; l1 minimization ; nonparametric function estimation ; optimal rate of convergence ; sparsity ; wavelets
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Abstract:
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The analysis of high-dimensional data sets now commonly arising in scientific investigations poses many statistical challenges not present in smaller scale studies. Extracting information with precision from such data is becoming ever more important. The needs for this research have attracted much interest in both in theoretical and applied statistics. In this talk I will discuss several inter-related problems in high-dimensional inference including nonparametric function estimation, sparse signal recovery (compressed sensing) and estimation of large covariance matrices.
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