[2107.01742] Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Methodology arXiv:2107.01742 (stat) [Submitted on 4 Jul 2021 ( v1 ), last revised 30 Apr 2026 (this version, v2)] Title: Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation Authors: Gordon J. Ross View a PDF of the paper titled Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation, by Gordon J. Ross View PDF HTML (experimental) Abstract: Change point methods are used to divide a sequence of observations into segments with different behaviour. Often, the distributional form of the observations is unknown, but the changes of interest are likely to involve shifts in location, scale, or both. We consider the problem of detecting multiple change points in a sequence without specifying a parametric model for the data. We propose the WBS-Lepage procedure, a nonparametric method which combines wild binary segmentation with a rank-based Lepage statistic. The statistic is formed from Mann--Whitney and Mood components, which are respectively sensitive to changes in location and scale. Since it depends on the observations only through their ranks, its null distribution is distribution-free. This allows finite-sample thresholds to be calibrated by Monte Carlo simulation, providing direct control over the probability of falsely detecting change points when none exist. We compare WBS-Lepage with existing nonparametric change point methods, including penalised likelihood and binary-segmentation-based competitors. The proposed method performs competitively for location changes and is particularly effective for detecting changes in scale. We illustrate the procedure on a stylometric analysis of changes in an author's writing style and provide an implementation of our method in the accompanying R package npwbs. Subjects: Methodology (stat.ME) ; Computation (stat.CO) Cite as: arXiv:2107.01742 [stat.ME] (or arXiv:2107.01742v2 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2107.01742 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Gordon Ross J [ view email ] [v1] Sun, 4 Jul 2021 22:17:14 UTC (1,016 KB) [v2] Thu, 30 Apr 2026 18:12:52 UTC (1,143 KB) Full-text links: Access Paper: View a PDF of the paper titled Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation, by Gordon J. Ross View PDF HTML (experimental) TeX Source view license Current browse context: stat.ME < prev | next > new | recent | 2021-07 Change to browse by: stat stat.CO References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from