Abstract Details
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Activity Number:
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280
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Type:
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Invited
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Date/Time:
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Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #307242 |
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Title:
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Principal Component Analysis for High-Dimensional Non-Gaussian Data
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Author(s):
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Fang Han and Han Liu*+
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Companies:
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Johns Hopkins University and Princeton University
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Keywords:
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High Dimensional Statistics ;
Principal Component Analysis ;
Elliptical Distribution ;
Transelliptical Distribution ;
Robust Statistics
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Abstract:
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We introduce new principal component analysis methods for analyzing high dimensional non-Gaussian data. In particular, we assume the data follow an elliptical distribution or transelliptical distribution. Using either marginal or multivariate ranks, our estimators attain the optimal rates of convergence in parameter estimation. We also discuss the computational aspects of the proposed estimators. This is a joint work with Fang Han.
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Authors who are presenting talks have a * after their name.
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