Change-Point Estimation in High-Dimensional Markov Random Field Models

Sandipan Roy, Yves Atchade, George Michailidis

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
47 Downloads (Pure)

Abstract

This paper investigates a change-point estimation problem in the context of high-dimensional Markov Random Field models. Change-points represent a key feature in many dynamically evolving network structures. The change-point estimate is obtained by maximizing a profile penalized pseudo-likelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logarithmic factor, even in settings where the number of possible edges in the network far exceeds the sample size. The performance of the proposed estimator is evaluated on synthetic data sets and is also used to explore voting patterns in the US Senate in the 1979-2012 period.
Original languageEnglish
Pages (from-to)1187 - 1206
Number of pages20
JournalJournal of the Royal Statistical Society: Series B - Statistical Methodology
Volume79
Issue number4
Early online date26 Sep 2016
DOIs
Publication statusPublished - 1 Sep 2017

Keywords

  • stat.ME

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