Testing for a change in mean after changepoint detection

• 成果类型: Article
• 署名作者: Jewell, Sean; Fearnhead, Paul; Witten, Daniela
• 署名单位: University of Washington; University of Washington Seattle; Lancaster University; University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12501
• 发表日期: 2022
• 页码: 1082-1104
• 关键词: Post-selection Inference; False Discovery Rate; Change-point Detection; spike train inference; binary segmentation; algorithm; sequence

摘要

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 -inference/.