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Activity Number: 515 - Visualization for Distributions, Networks and Statistical Inference
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Graphics
Abstract #304178
Title: Semiparametric Dynamic Adaptive Robust Estimations for High-Dimensional Networks
Author(s): Tzu-Chun Wu* and Emily Lei Kang
Companies: University of Cincinnati and University of Cincinnati
Keywords: Bandwidth Selection; Conditional Independence; Dynamic Graphical Model; Precision Matrix Estimation; Sparse Network

Precision matrices are widely used to depict the dependence structure within a network in many applications. We propose a semiparametric method to estimate dynamic precision matrices in the high-dimensional setting. Our method for dynamic network estimation combines not only possesses the flexibility to characterize the changing precision matrix, but also provide robust inference under various distribution assumptions. An efficient algorithm via linear programming is developed to implement the method. We demonstrate the performance and advantages of the proposed method with extensive numerical studies.

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

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