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  • 2107.10572v1

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Computational and Graphical Statistics on 12/01/2022, available online: http://www.tandfonline.com/10.1080/10618600.2021.2000873

    Accepted author manuscript, 6.15 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License


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Graphical Influence Diagnostics for Changepoint Models

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>3/07/2022
<mark>Journal</mark>Journal of Computational and Graphical Statistics
Issue number3
Number of pages13
Pages (from-to)753-765
Publication StatusPublished
Early online date8/11/21
<mark>Original language</mark>English


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.