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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/Magazine › Journal article › peer-review

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Graphical Influence Diagnostics for Changepoint Models. / Wilms, Ines; Killick, Rebecca; Matteson, David S.
In: Journal of Computational and Graphical Statistics, Vol. 31, No. 3, 03.07.2022, p. 753-765.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wilms, I, Killick, R & Matteson, DS 2022, 'Graphical Influence Diagnostics for Changepoint Models', Journal of Computational and Graphical Statistics, vol. 31, no. 3, pp. 753-765. https://doi.org/10.1080/10618600.2021.2000873

APA

Wilms, I., Killick, R., & Matteson, D. S. (2022). Graphical Influence Diagnostics for Changepoint Models. Journal of Computational and Graphical Statistics, 31(3), 753-765. https://doi.org/10.1080/10618600.2021.2000873

Vancouver

Wilms I, Killick R, Matteson DS. Graphical Influence Diagnostics for Changepoint Models. Journal of Computational and Graphical Statistics. 2022 Jul 3;31(3):753-765. Epub 2021 Nov 8. doi: 10.1080/10618600.2021.2000873

Author

Wilms, Ines ; Killick, Rebecca ; Matteson, David S. / Graphical Influence Diagnostics for Changepoint Models. In: Journal of Computational and Graphical Statistics. 2022 ; Vol. 31, No. 3. pp. 753-765.

Bibtex

@article{01741c4d2ed744a3907b56d1996b9b1b,
title = "Graphical Influence Diagnostics for Changepoint Models",
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.",
keywords = "Change point, Influential data, Segmentation, Statistical graphics, Structural change, Visual diagnostics",
author = "Ines Wilms and Rebecca Killick and Matteson, {David S.}",
year = "2022",
month = jul,
day = "3",
doi = "10.1080/10618600.2021.2000873",
language = "English",
volume = "31",
pages = "753--765",
journal = "Journal of Computational and Graphical Statistics",
issn = "1061-8600",
publisher = "American Statistical Association",
number = "3",

}

RIS

TY - JOUR

T1 - Graphical Influence Diagnostics for Changepoint Models

AU - Wilms, Ines

AU - Killick, Rebecca

AU - Matteson, David S.

PY - 2022/7/3

Y1 - 2022/7/3

N2 - 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.

AB - 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.

KW - Change point

KW - Influential data

KW - Segmentation

KW - Statistical graphics

KW - Structural change

KW - Visual diagnostics

U2 - 10.1080/10618600.2021.2000873

DO - 10.1080/10618600.2021.2000873

M3 - Journal article

VL - 31

SP - 753

EP - 765

JO - Journal of Computational and Graphical Statistics

JF - Journal of Computational and Graphical Statistics

SN - 1061-8600

IS - 3

ER -