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Latent class models for mixed variables with applications in Archaeometry

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  • Moustaki, Irini
  • Papageorgiou, Ioulia

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  • Moustaki, Irini & Papageorgiou, Ioulia, 2005. "Latent class models for mixed variables with applications in Archaeometry," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 659-675, March.
    • Handle: RePEc:eee:csdana:v:48:y:2005:i:3:p:659-675
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    References listed on IDEAS

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    1. Paul Marriott, 1995. "8. An Introduction to the Bootstrap," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(2), pages 347-347, March.
    2. Everitt, B. S., 1988. "A finite mixture model for the clustering of mixed-mode data," Statistics & Probability Letters, Elsevier, vol. 6(5), pages 305-309, April.
    3. Chris Fraley & Adrian E. Raftery, 1999. "MCLUST: Software for Model-Based Cluster Analysis," Journal of Classification, Springer;The Classification Society, vol. 16(2), pages 297-306, July.
    4. David J. Bartholomew & Panagiota Tzamourani, 1999. "The Goodness of Fit of Latent Trait Models in Attitude Measurement," Sociological Methods & Research, , vol. 27(4), pages 525-546, May.
    5. Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer;The Classification Society, vol. 12(1), pages 21-55, March.
    6. Stanley Sclove, 1987. "Application of model-selection criteria to some problems in multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 333-343, September.
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    Cited by:

    1. Nalbarte, Laura & ALTMARK, SILVIA & Massa, Fernando, 2022. "Identificación de tipologı́a de pobreza multidimensional a través del enfoque de cluster probabilı́stico (Identification of typology of multidimensional poverty through the probabilistic cluster appro," SocArXiv nv962, Center for Open Science.
    2. Natalia Casado-Sanz & Begoña Guirao & Maria Attard, 2020. "Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective," Sustainability, MDPI, vol. 12(6), pages 1-26, March.
    3. Zhang, Q. & Ip, E.H., 2014. "Variable assessment in latent class models," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 146-156.
    4. Christophe Biernacki & Alexandre Lourme, 2019. "Unifying data units and models in (co-)clustering," 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. 13(1), pages 7-31, March.
    5. Renuka Mahadevan & Vincent Hoang, 2016. "Is There a Link Between Poverty and Food Security?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 128(1), pages 179-199, August.
    6. Luca De Angelis, 2013. "Latent class models for financial data analysis: some statistical developments," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 227-242, June.
    7. Christophe Biernacki & Matthieu Marbac & Vincent Vandewalle, 2021. "Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 129-157, April.
    8. Marbac, Matthieu & Vandewalle, Vincent, 2019. "A tractable multi-partitions clustering," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 167-179.

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