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A hierarchical mixture model for clustering three-way data sets

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  • Vermunt, Jeroen K.

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  • Vermunt, Jeroen K., 2007. "A hierarchical mixture model for clustering three-way data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5368-5376, July.
    • Handle: RePEc:eee:csdana:v:51:y:2007:i:11:p:5368-5376
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    References listed on IDEAS

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    1. Kaye Basford & Geoffrey McLachlan, 1985. "The mixture method of clustering applied to three-way data," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 109-125, December.
    2. Leonhard Knorr-Held & Günter Raßer, 2000. "Bayesian Detection of Clusters and Discontinuities in Disease Maps," Biometrics, The International Biometric Society, vol. 56(1), pages 13-21, March.
    3. Vermunt, Jeroen K. & Magidson, Jay, 2003. "Latent class models for classification," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 531-537, January.
    4. Lynette A. Hunt & Kaye E. Basford, 1999. "Fitting a Mixture Model to Three-Mode Three-Way Data with Categorical and Continuous Variables," Journal of Classification, Springer;The Classification Society, vol. 16(2), pages 283-296, July.
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    Citations

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    Cited by:

    1. Calò, Daniela G. & Viroli, Cinzia, 2010. "A dimensionally reduced finite mixture model for multilevel data," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2543-2553, November.
    2. Calò, Daniela G. & Montanari, Angela & Viroli, Cinzia, 2014. "A hierarchical modeling approach for clustering probability density functions," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 79-91.
    3. Simon Blanchard & Wayne DeSarbo, 2013. "A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification," Psychometrika, Springer;The Psychometric Society, vol. 78(2), pages 322-340, April.
    4. Michel Meulders & Francis Tuerlinckx & Wolf Vanpaemel, 2013. "Constrained Multilevel Latent Class Models for the Analysis of Three-Way Three-Mode Binary Data," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 306-337, October.
    5. Federico Ferraccioli & Giovanna Menardi, 2023. "Modal clustering of matrix-variate data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 323-345, June.
    6. Pieter C. Schoonees & Patrick J. F. Groenen & Michel Velden, 2022. "Least-squares bilinear clustering of three-way data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 1001-1037, December.
    7. Meulders, Michel, 2013. "An R Package for Probabilistic Latent Feature Analysis of Two-Way Two-Mode Frequencies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i14).
    8. Vicari, Donatella & Alfó, Marco, 2014. "Model based clustering of customer choice data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 3-13.
    9. Lingzhe Guo & Reza Modarres, 2020. "Testing the equality of matrix distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 289-307, June.
    10. Leonard Paas & Tammo Bijmolt & Jeroen Vermunt, 2015. "Long-term developments of respondent financial product portfolios in the EU: a multilevel latent class analysis," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 249-262, August.
    11. Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.

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