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Model-based clustering of functional data via mixtures of t distributions

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dc.contributor.authorAnton, Cristina
dc.contributor.authorSmith, Iain
dc.date.accessioned2024-01-29T21:23:25Z
dc.date.available2024-01-29T21:23:25Z
dc.date.issued2023
dc.description.abstractWe propose a procedure, called T-funHDDC, for clustering multivariate functional data with outliers which extends the functional high dimensional data clustering (funHDDC) method (Schmutz et al, 2020) by considering a mixture of multivariate t distributions. We de ne a family of latent mixture models following the approach used for the parsimonious models considered in funHDDC and also constraining or not the degrees of freedom of the multivariate t distributions to be equal across the mixture components. The parameters of these models are estimated using an expectation maximization (EM) algorithm. In addition to proposing the T-funHDDC method, we add a family of parsimonious models to C-funHDDC, which is an alternative method for clustering multivariate functional data with outliers based on a mixture of contaminated normal distributions (Amovin-Assagba et al, 2022). We compare T-funHDDC, C-funHDDC, and other existing methods on simulated functional data with outliers and for real-world data. T-funHDDC out-performs funHDDC when applied to functional data with outliers, and its good performance makes it an alternative to C-funHDDC. We also apply the T-funHDDC method to the analysis of traffic flow in Edmonton, Canada.
dc.identifier.citationAnton, C., & Smith, I. (2023). Model-based clustering of functional data via mixtures of t distributions. Advances in Data Analysis and Classification. https://doi.org/10.1007/s11634-023-00542-w
dc.identifier.doihttps://doi.org/10.1007/s11634-023-00542-w
dc.identifier.urihttps://hdl.handle.net/20.500.14078/3399
dc.language.isoen
dc.rightsAll Rights Reserved
dc.subjectfunctional data analysis
dc.subjectmodel-based clustering
dc.subjectmultivariate t distributions
dc.subjectEM algorithm
dc.subjectmultivariate functional principal components analysis
dc.titleModel-based clustering of functional data via mixtures of t distributionsen
dc.typeArticle Post-Print

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