Roy, S;
Atchadé, Y;
Michailidis, G;
(2017)
Change point estimation in high dimensional Markov random-field models.
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
, 79
(4)
pp. 1187-1206.
10.1111/rssb.12205.
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Abstract
The 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 pseudolikelihood 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 estimator proposed is evaluated on synthetic data sets and is also used to explore voting patterns in the US Senate in the 1979-2012 period.
| Type: | Article |
|---|---|
| Title: | Change point estimation in high dimensional Markov random-field models |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/rssb.12205 |
| Publisher version: | http://dx.doi.org/10.1111/rssb.12205 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Change point analysis; High dimensional inference; Markov random fields; Network analysis; Profile pseudolikelihood |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1539751 |
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