Abstract Details
Activity Number:
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548
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Type:
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Contributed
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Date/Time:
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Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #315380
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Title:
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Nonparametric Network Denoising
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Author(s):
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Yuan Zhang* and Elizaveta Levina and Ji Zhu
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Companies:
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University of Michigan and University of Michigan and University of Michigan
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Keywords:
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networks ;
nonparametric statistics
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Abstract:
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In this work we address the problem of estimating probability matrices of exchangeable networks. We propose a distributive nonparametric method that can estimate edge probabilities of an arbitrary subgraph or the entire network. Under proper choice of the tuning parameter, our method is consistent with a competitive rate for networks generated from a graphon that is piece-wise Lipschitz on finite number of blocks. Numerical studies show the high accuracy of our method compared to benchmark methods.
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Authors who are presenting talks have a * after their name.
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