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Activity Number: 548
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #315380
Title: Nonparametric Network Denoising
Author(s): Yuan Zhang* and Elizaveta Levina and Ji Zhu
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: networks ; nonparametric statistics
Abstract:

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.

Authors who are presenting talks have a * after their name.

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