SEQUENTIAL CHANGE-POINT DETECTION BASED ON NEAREST NEIGHBORS
SEQUENTIAL CHANGE-POINT DETECTION BASED ON NEAREST NEIGHBORS
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DOI：
10.1214/18-aos1718
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发表时间：
2019-06-01
影响因子：
4.5
通讯作者：
Chen, Hao
中科院分区：
文献类型：
--
作者：
Chen, Hao
我们为在线，顺序数据分析中检测更改点的新框架提出了一个新的框架。该方法利用最近的邻居信息，可以应用于多元观测值的序列或非欧盟数据对象（例如网络数据）。探索了不同的停止规则，并且由于其理想的属性而建议使用一个具体规则。对于推荐的规则，得出了平均运行长度的准确分析近似，这使其成为真实多元/对象顺序数据监视应用程序的简单现成方法。模拟显示，对于高维数据，新方法的性能比基于可能性的方法更好。通过真实数据集说明了新方法，以检测社交网络的全球结构变化。
We propose a new framework for the detection of change-points in online, sequential data analysis. The approach utilizes nearest neighbor information and can be applied to sequences of multivariate observations or non-Euclidean data objects, such as network data. Different stopping rules are explored, and one specific rule is recommended due to its desirable properties. An accurate analytic approximation of the average run length is derived for the recommended rule, making it an easy off-the-shelf approach for real multivariate/object sequential data monitoring applications. Simulations reveal that the new approach has better performance than likelihood-based approaches for high dimensional data. The new approach is illustrated through a real dataset in detecting global structural changes in social networks.