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Graphical Influence Diagnostics for Changepoint Models

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print

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Graphical Influence Diagnostics for Changepoint Models. / Wilms, Ines; Killick, Rebecca; Matteson, David S.

In: Journal of Computational and Graphical Statistics, 08.11.2021.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Wilms, I, Killick, R & Matteson, DS 2021, 'Graphical Influence Diagnostics for Changepoint Models', Journal of Computational and Graphical Statistics. https://doi.org/10.1080/10618600.2021.2000873

APA

Wilms, I., Killick, R., & Matteson, D. S. (2021). Graphical Influence Diagnostics for Changepoint Models. Journal of Computational and Graphical Statistics. 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. 2021 Nov 8. https://doi.org/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. 2021.

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. ",
keywords = "Change point, Influential data, Segmentation, Statistical graphics, Structural change, Visual diagnostics",
author = "Ines Wilms and Rebecca Killick and Matteson, {David S.}",
year = "2021",
month = nov,
day = "8",
doi = "10.1080/10618600.2021.2000873",
language = "English",
journal = "Journal of Computational and Graphical Statistics",
issn = "1061-8600",
publisher = "American Statistical Association",

}

RIS

TY - JOUR

T1 - Graphical Influence Diagnostics for Changepoint Models

AU - Wilms, Ines

AU - Killick, Rebecca

AU - Matteson, David S.

PY - 2021/11/8

Y1 - 2021/11/8

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.

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.

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

JO - Journal of Computational and Graphical Statistics

JF - Journal of Computational and Graphical Statistics

SN - 1061-8600

ER -