Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 583 - Learning Network Structure in Heterogeneous Populations
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313356
Title: Fast Algorithms for Detection of Structural Breaks in High-Dimensional Data
Author(s): George Michailidis*
Companies: University of Florida
Keywords: fast algorithms; high-dimensional data; time series; block segmentation scheme

It is of interest to detect structural breaks in high dimensional time series data, together with the parameters of the statistical model employed to capture the relationships amongst the variables/features of interest. An additional challenge emerges in the presence of very large data sets, namely on how to accomplish these two objectives in a computational efficient manner. We outline a novel procedure which leverages a block segmentation scheme (BSS) that reduces the number of model parameters to be estimated through a regularized least squares criterion. Specifically, BSS examines appropriately defined blocks of the available data, which when combined with a fused lasso based estimation criterion, leads to significant computational gains without compromising on the statistical accuracy in identifying the number and location of the structural breaks. The procedure together with additional screening steps consistently estimates the number and location of break points. It is further applicable to various statistical models, including regression, graphical models and vector-autoregressive models. Extensive numerical work on synthetic data supports the theoretical findings.

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

Back to the full JSM 2020 program