Abstract

Changepoint models enjoy a wide appeal in a variety of disciplines to model the heterogeneity of ordered data. Graphical influence diagnostics to characterize the influence of single observations on changepoint models are, however, lacking. We address this gap by developing a framework for investigating instabilities in changepoint segmentations and assessing the influence of single observations on various outputs of a changepoint analysis. We construct graphical diagnostic plots that allow practitioners to assess whether instabilities occur; how and where they occur; and to detect influential individual observations triggering instability. We analyze well-log data to illustrate how such influence diagnostic plots can be used in practice to reveal features of the data that may otherwise remain hidden. Supplementary materials for this article are available online.

摘要

Changepoint 模型在对有序数据的异质性建模的各种学科中具有广泛的吸引力。然而，缺乏图形影响诊断来描述单个观察对变化点模型的影响。我们通过开发一个框架来解决这一差距，该框架用于调查变化点分割中的不稳定性并评估单个观察结果对变化点分析的各种输出的影响。我们构建了图形诊断图，使从业者能够评估是否发生不稳定；它们发生的方式和地点；并检测有影响力的个人观察触发不稳定性。我们分析测井数据来说明如何在实践中使用这种影响诊断图来揭示数据的特征，否则这些特征可能会保持隐藏。