Graphical Influence Diagnostics for Changepoint Models. Issue 3 (3rd July 2022)

• Record Type: Journal Article
• Authors: Wilms, Ines; Killick, Rebecca; Matteson, David S.
• Is Part Of: Journal of computational and graphical statistics. Volume 31:Issue 3(2022)
• Page Start: 753
• Page End: 765
• Publication Date: 2022-07-03
• Subjects: Change point -- Influential data -- Segmentation -- Statistical graphics -- Structural change -- Visual diagnostics; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2021.2000873
• Languages: English
• ISSNs: 1061-8600

Abstract

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.