Graphical Influence Diagnostics for Changepoint Models

Ines Wilms, Rebecca Killick, David S. Matteson

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Number of pages13
JournalJournal of Computational and Graphical Statistics
DOIs
Publication statusE-pub ahead of print - 9 Jan 2022

Keywords

  • change point
  • influential data
  • segmentation
  • statistical graphics
  • structural change
  • visual diagnostics
  • Influential data
  • Statistical graphics
  • Segmentation
  • Change point
  • POINT DETECTION
  • Visual diagnostics
  • SEGMENTATION
  • Structural change

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