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Multiresolution clustering of dependent functional data with application to climate reconstruction
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0001-7917-5687
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2019 (English)In: Stat, E-ISSN 2049-1573, Vol. 8, no 1, article id e240Article in journal (Refereed) Published
Abstract [en]

We propose a new nonparametric clustering method for dependent functional data, the double clustering bagging Voronoi method. It consists of two levels of clustering. Given a spatial lattice of points, a function is observed at each grid point. In the first‐level clustering, features of the functional data are clustered. The second‐level clustering takes dependence into account, by grouping local representatives, built from the resulting first‐level clusters, using a bagging Voronoi strategy. Depending on the distance measure used, features of the functions may be included in the second‐step clustering, making the method flexible and general. Combined with the clustering method, a multiresolution approach is proposed that searches for stable clusters at different spatial scales, aiming to capture latent structures. This provides a powerful and computationally efficient tool to cluster dependent functional data at different spatial scales, here illustrated by a simulation study. The introduced methodology is applied to varved lake sediment data, aiming to reconstruct winter climate regimes in northern Sweden at different time resolutions over the past 6,000 years.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019. Vol. 8, no 1, article id e240
Keywords [en]
bagging Voronoi strategy, climate reconstruction, clustering, dependency, functional data
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-164004DOI: 10.1002/sta4.240OAI: oai:DiVA.org:umu-164004DiVA, id: diva2:1360319
Funder
Swedish Research Council, 340-2013-5203Swedish Research Council, 2016-02763Available from: 2019-10-11 Created: 2019-10-11 Last updated: 2019-10-14Bibliographically approved

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Abramowicz, KonradSchelin, LinaSjöstedt de Luna, SaraStrandberg, Johan

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  • apa
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