A Scalable Algorithm for Gaussian Graphical Models with ChangePoints
Abstract
Graphical models with changepoints 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 majorizeminimize (MM) algorithm that can be useful for computing changepoints 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 changepoint. 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 20002016.
 Publication:

arXiv eprints
 Pub Date:
 July 2017
 arXiv:
 arXiv:1707.04306
 Bibcode:
 2017arXiv170704306A
 Keywords:

 Statistics  Methodology
 EPrint:
 39 pages, 13 figures