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.

中文翻译：

---

高维马尔可夫随机场模型中的变化点估计。

本文研究了高维马尔可夫随机场模型中的变化点估计问题。变更点代表了许多动态发展的网络结构中的关键功能。通过在稀疏性假设下最大化轮廓罚分伪似然函数来获得变化点估计。即使在网络中可能边缘的数量远远超过样本大小的设置中，我们也可以得出估计的严格界限，直到对数因子为止。拟议估算器的性能在综合数据集上进行了评估，还用于探索1979-2012年期间美国参议院的投票模式。