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Nonparametric bagging clustering methods to identify latent structures from a sequence of dependent categorical data
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0002-9040-6674
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0003-1591-5716
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0003-1098-0076
2022 (English)In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 177, article id 107583Article in journal (Refereed) Published
Abstract [en]

Nonparametric bagging clustering methods are studied and compared to identify latent structures from a sequence of dependent categorical data observed along a one-dimensional (discrete) time domain. The frequency of the observed categories is assumed to be generated by a (slowly varying) latent signal, according to latent state-specific probability distributions. The bagging clustering methods use random tessellations (partitions) of the time domain and clustering of the category frequencies of the observed data in the tessellation cells to recover the latent signal, within a bagging framework. New and existing ways of generating the tessellations and clustering are discussed and combined into different bagging clustering methods. Edge tessellations and adaptive tessellations are the new proposed ways of forming partitions. Composite methods are also introduced, that are using (automated) decision rules based on entropy measures to choose among the proposed bagging clustering methods. The performance of all the methods is compared in a simulation study. From the simulation study it can be concluded that local and global entropy measures are powerful tools in improving the recovery of the latent signal, both via the adaptive tessellation strategies (local entropy) and in designing composite methods (global entropy). The composite methods are robust and overall improve performance, in particular the composite method using adaptive (edge) tessellations.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 177, article id 107583
Keywords [en]
Bagging methods, Categorical dependent data, Clustering, Entropy
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-198931DOI: 10.1016/j.csda.2022.107583ISI: 000930488900007Scopus ID: 2-s2.0-85135796679OAI: oai:DiVA.org:umu-198931DiVA, id: diva2:1696677
Funder
Swedish Research Council, 340-2013-5203Available from: 2022-09-19 Created: 2022-09-19 Last updated: 2023-09-05Bibliographically approved

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Abramowicz, KonradSjöstedt de Luna, SaraStrandberg, Johan

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CiteExportLink to record
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