Abstract

We propose estimation methods for change points in high-dimensional covariance structures with an emphasis on challenging scenarios with missing values. We advocate three imputation like methods and investigate their implications on common losses used for change-point detection. We also discuss how model selection methods have to be adapted to the setting of incomplete data. The methods are compared in a simulation study and applied to a time series from an environmental monitoring system. An implementation of our proposals within the R-package hdcd is available via the online supplementary materials.

中文翻译：

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存在缺失值时图形模型的变化点检测

摘要

我们提出了高维协方差结构中变化点的估计方法，重点是具有缺失值的具有挑战性的场景。我们提倡三种类似插补的方法，并研究它们对用于变化点检测的常见损失的影响。我们还讨论了模型选择方法必须如何适应不完整数据的设置。这些方法在模拟研究中进行了比较，并应用于来自环境监测系统的时间序列。我们的建议在 R 包 hdcd 中的实施可通过在线补充材料获得。