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
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
| <mark>Journal publication date</mark> | 3/07/2022 |
|---|---|
| <mark>Journal</mark> | Journal of Computational and Graphical Statistics |
| Issue number | 3 |
| Volume | 31 |
| Number of pages | 13 |
| Pages (from-to) | 753-765 |
| Publication Status | Published |
| Early online date | 8/11/21 |
| <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.