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Activity Number:
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73
- Nonparametric Statistics in High-Dimensional Settings
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Type:
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Contributed
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Date/Time:
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Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #323233
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View Presentation
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Title:
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Sparse Covariance Estimation via Concentration Inequalities
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Author(s):
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Adam Kashlak* and Linglong Kong
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Companies:
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Univ of Cambridge and University of Alberta
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Keywords:
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sparse estimation ;
covariance matrix ;
concentration inequality ;
high dimensional data
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Abstract:
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We introduce a new approach to the estimation of sparse covariance matrices for high dimensional data making use of concentration inequalities. The inequalities are used to a construct nonasymptotic confidence set about the empirical estimate. We then apply a search procedure to find an optimally sparse member of that set. This approach takes inspiration and can, in some sense, be thought of as both a penalized estimator and threshold estimator. The method is shown to have excellent performance when compared to both lasso and hard and soft threshold estimators for the covariance matrix.
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Authors who are presenting talks have a * after their name.
