Clustering longitudinal ordinal data via finite mixture of matrix-variate distributions
Author:
Funder
Agence Nationale de la Recherche
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11222-024-10390-z.pdf
Reference53 articles.
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3. Anderlucci, L., Viroli, C.: Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data. Ann. Appl. Stat. 9(2), 777–800 (2015). https://doi.org/10.1214/15-AOAS816
4. Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: SODA ’07: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics, USA (2007). https://doi.org/10.5555/1283383.1283494
5. Basford, K.E., McLachlan, G.J.: The mixture method of clustering applied to three-way data. J. Classif. 2(1), 109–125 (1985). https://doi.org/10.1007/BF01908066
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