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A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model

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  • Isabella Morlini

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  • Isabella Morlini, 2012. "A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model," 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. 6(1), pages 5-28, April.
    • Handle: RePEc:spr:advdac:v:6:y:2012:i:1:p:5-28
    DOI: 10.1007/s11634-011-0101-z
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    References listed on IDEAS

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    1. 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.
    2. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    3. Ulf Olsson, 1979. "Maximum likelihood estimation of the polychoric correlation coefficient," Psychometrika, Springer;The Psychometric Society, vol. 44(4), pages 443-460, December.
    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. Anil Chaturvedi & Paul E. Green & J. Douglas Caroll, 2001. "K-modes Clustering," Journal of Classification, Springer;The Classification Society, vol. 18(1), pages 35-55, January.
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    Cited by:

    1. Marek Śmieja & Magdalena Wiercioch, 2017. "Constrained clustering with a complex cluster structure," 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. 11(3), pages 493-518, September.
    2. Ranalli, Monia & Rocci, Roberto, 2017. "Mixture models for mixed-type data through a composite likelihood approach," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 87-102.

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