Roy Sandipan, Atchadé Yves, Michailidis George
University College London, UK.
University of Michigan, Ann Arbor, USA.
J R Stat Soc Series B Stat Methodol. 2017 Sep;79(4):1187-1206. doi: 10.1111/rssb.12205. Epub 2016 Sep 26.
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.
本文研究了高维马尔可夫随机场模型背景下的变点估计问题。变点是许多动态演化网络结构中的关键特征。通过在稀疏性假设下最大化一个轮廓惩罚伪似然函数来获得变点估计。即使在网络中可能边的数量远远超过样本量的情况下，我们也能推导出估计的一个紧密界，误差至多为一个对数因子。在合成数据集上评估了所提出估计器的性能，并将其用于探索1979 - 2012年期间美国参议院的投票模式。
