A Scalable Algorithm for Gaussian Graphical Models with Change-Points

Abstract

Graphical models with change-points are computationally challenging to fit, particularly in cases where the number of observation points and the number of nodes in the graph are large. Focusing on Gaussian graphical models, we introduce an approximate majorize-minimize (MM) algorithm that can be useful for computing change-points in large graphical models. The proposed algorithm is an order of magnitude faster than a brute force search. Under some regularity conditions on the data generating process, we show that with high probability, the algorithm converges to a value that is within statistical error of the true change-point. A fast implementation of the algorithm using Markov Chain Monte Carlo is also introduced. The performances of the proposed algorithms are evaluated on synthetic data sets and the algorithm is also used to analyze structural changes in the S&P 500 over the period 2000-2016.

39 pages, 13 figures

References in corpus (2)

• Pathwise coordinate optimization
• Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation

Cited by in corpus (5)

• Sequential change-point detection in high-dimensional Gaussian graphical models
• Detection and estimation of parameters in high dimensional multiple change point regression models via ${\ell }_1/{\ell }_0$ regularization and discrete optimization
• Inference on the Change Point for High Dimensional Dynamic Graphical Models
• Segmentation of high dimensional means over multi-dimensional change points and connections to regression trees
• Online detection of local abrupt changes in high-dimensional Gaussian graphical models