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Testing for a change in mean after changepoint detection

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  • Sean Jewell
  • Paul Fearnhead
  • Daniela Witten

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, ℓ0 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/.

Suggested Citation

  • Sean Jewell & Paul Fearnhead & Daniela Witten, 2022. "Testing for a change in mean after changepoint detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1082-1104, September.
    • Handle: RePEc:bla:jorssb:v:84:y:2022:i:4:p:1082-1104
    DOI: 10.1111/rssb.12501
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    References listed on IDEAS

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    Cited by:

    1. Tuomas Rajala & Petteri Packalen & Mari Myllymäki & Annika Kangas, 2023. "Improving Detection of Changepoints in Short and Noisy Time Series with Local Correlations: Connecting the Events in Pixel Neighbourhoods," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(3), pages 564-590, September.

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