|
Activity Number:
|
545
- Statistical Advances in Learning Large-Scale Networks from Massive Data Sets
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Statistical Learning and Data Science
|
|
Abstract #323321
|
|
|
Title:
|
Frequency-Domain Graphical Modeling of Large-Scale Time Series
|
|
Author(s):
|
Sumanta Basu* and Navonil Deb
|
|
Companies:
|
Cornell University and Cornell University
|
|
Keywords:
|
graphical model;
time series;
spectral density;
lasso;
network
|
|
Abstract:
|
Graphical models offer a powerful framework for capturing intertemporal and contemporaneous relationships among the components of a multivariate time series. These relationships are encoded in the multivariate spectral density matrix and its inverse. We will present adaptive thresholding and penalization methods for estimation of these objects under suitable sparsity assumptions. We will discuss new optimization algorithms and investigate consistency of estimation under a double-asymptotic regime where the dimension of the time series increases with sample size.
|
Authors who are presenting talks have a * after their name.
