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The proposed method is fully non-parametric, robust to temporal dependence and avoids the demanding consistent estimation of long-run variance. One salient and distinct feature of the proposed method is its versatility, where it allows change-point detection for a broad class of parameters (such as mean, variance, correlation and quantile) in a unified fashion. At the core of our method, we couple the self-normalisation- (SN) based tests with a novel nested local-window segmentation algorithm, which seems new in the growing literature of change-point analysis. Due to the presence of an inconsistent long-run variance estimator in the SN test, non-standard theoretical arguments are further developed to derive the consistency and convergence rate of the proposed SN-based change-point detection method. Extensive numerical experiments and relevant real data analysis are conducted to illustrate the effectiveness and broad applicability of our proposed method in comparison with state-of-the-art approaches in the literature.<\/jats:p>","DOI":"10.1111\/rssb.12552","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T22:59:45Z","timestamp":1667170785000},"page":"1699-1725","source":"Crossref","is-referenced-by-count":13,"title":["Segmenting Time Series via Self-Normalisation"],"prefix":"10.1093","volume":"84","author":[{"given":"Zifeng","family":"Zhao","sequence":"first","affiliation":[{"name":"Mendoza College of Business , , Notre Dame , Indiana , USA"},{"name":"University of Notre Dame , , Notre Dame , Indiana , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feiyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science , , Shanghai , China"},{"name":"Fudan University , , Shanghai 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