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Ordinary Ward's linkage defines the cluster dissimilarity between two clusters as the increase in the sum of squared errors (SSE) upon merging, which can be interpreted graphically as an increase in within\u2010cluster dispersion. Motivated by this principle, the proposed linkages measure the weighted increase in band width required to construct a common band containing the central representatives of two candidate clusters. To reduce the influence of outliers, the central curves are selected separately within each cluster by leveraging the magnitude\u2010shape outlyingness measure and the modified band depth, respectively. Simulations and two EEG data analyses demonstrate the robustness and competitive performance of the proposed methods under a range of contamination settings.<\/jats:p>","DOI":"10.1002\/sim.70771","type":"journal-article","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T10:55:52Z","timestamp":1791975352000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust Functional Ward's Linkages With Applications in\n                    <scp>EEG<\/scp>\n                    Data Clustering"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8098-2707","authenticated-orcid":false,"given":"Tianbo","family":"Chen","sequence":"first","affiliation":[{"name":"School of Big Data and Statistics Anhui University  Hefei 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