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Testing for a Change in Mean After Changepoint Detection

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Testing for a Change in Mean After Changepoint Detection. / Jewell, Sean; Fearnhead, Paul; Witten, Daniela.
In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 84, No. 4, 30.09.2022, p. 1082-1104.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Jewell, S, Fearnhead, P & Witten, D 2022, 'Testing for a Change in Mean After Changepoint Detection', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 84, no. 4, pp. 1082-1104. https://doi.org/10.1111/rssb.12501

APA

Jewell, S., Fearnhead, P., & Witten, D. (2022). Testing for a Change in Mean After Changepoint Detection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 84(4), 1082-1104. https://doi.org/10.1111/rssb.12501

Vancouver

Jewell S, Fearnhead P, Witten D. Testing for a Change in Mean After Changepoint Detection. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2022 Sept 30;84(4):1082-1104. Epub 2022 Apr 12. doi: 10.1111/rssb.12501

Author

Jewell, Sean ; Fearnhead, Paul ; Witten, Daniela. / Testing for a Change in Mean After Changepoint Detection. In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2022 ; Vol. 84, No. 4. pp. 1082-1104.

Bibtex

@article{9dfdd9a3df2a4a5eafcac2cd6f31b2af,
title = "Testing for a Change in Mean After Changepoint Detection",
abstract = "While many methods are available to detect structural changes in a time series, few procedures are available to quantify the uncertainty of these estimates post-detection. In this work, we fill this gap by proposing a new framework to test the null hypothesis that there is no change in mean around an estimated changepoint. We further show that it is possible to efficiently carry out this framework in the case of changepoints estimated by binary segmentation and its variants, (Formula presented.) segmentation, or the fused lasso. Our setup allows us to condition on much less information than existing approaches, which yields higher powered tests. We apply our proposals in a simulation study and on a dataset of chromosomal guanine-cytosine content. These approaches are freely available in the R package ChangepointInference at https://jewellsean.github.io/changepoint-inference/.",
keywords = "stat.ME",
author = "Sean Jewell and Paul Fearnhead and Daniela Witten",
year = "2022",
month = sep,
day = "30",
doi = "10.1111/rssb.12501",
language = "English",
volume = "84",
pages = "1082--1104",
journal = "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - Testing for a Change in Mean After Changepoint Detection

AU - Jewell, Sean

AU - Fearnhead, Paul

AU - Witten, Daniela

PY - 2022/9/30

Y1 - 2022/9/30

N2 - While many methods are available to detect structural changes in a time series, few procedures are available to quantify the uncertainty of these estimates post-detection. In this work, we fill this gap by proposing a new framework to test the null hypothesis that there is no change in mean around an estimated changepoint. We further show that it is possible to efficiently carry out this framework in the case of changepoints estimated by binary segmentation and its variants, (Formula presented.) segmentation, or the fused lasso. Our setup allows us to condition on much less information than existing approaches, which yields higher powered tests. We apply our proposals in a simulation study and on a dataset of chromosomal guanine-cytosine content. These approaches are freely available in the R package ChangepointInference at https://jewellsean.github.io/changepoint-inference/.

AB - While many methods are available to detect structural changes in a time series, few procedures are available to quantify the uncertainty of these estimates post-detection. In this work, we fill this gap by proposing a new framework to test the null hypothesis that there is no change in mean around an estimated changepoint. We further show that it is possible to efficiently carry out this framework in the case of changepoints estimated by binary segmentation and its variants, (Formula presented.) segmentation, or the fused lasso. Our setup allows us to condition on much less information than existing approaches, which yields higher powered tests. We apply our proposals in a simulation study and on a dataset of chromosomal guanine-cytosine content. These approaches are freely available in the R package ChangepointInference at https://jewellsean.github.io/changepoint-inference/.

KW - stat.ME

U2 - 10.1111/rssb.12501

DO - 10.1111/rssb.12501

M3 - Journal article

VL - 84

SP - 1082

EP - 1104

JO - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

JF - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

SN - 1369-7412

IS - 4

ER -