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A finite mixture model for the clustering of mixed-mode data

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  • Everitt, B. S.

Abstract

This paper presents a finite mixture density which might be a potentially useful model for the clustering of mixed mode data. A simplex algorithm is used to obtain maximum likelihood estimates and several small scale numerical examples indicate that its performance is relatively satisfactory.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:6:y:1988:i:5:p:305-309
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    Cited by:

    1. 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.
    2. William H. Greene & David A. Hensher, 2013. "Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model," Applied Economics, Taylor & Francis Journals, vol. 45(14), pages 1897-1902, May.
    3. William H. Greene & David A. Hensher, 2008. "Modeling Ordered Choices: A Primer and Recent Developments," Working Papers 08-26, New York University, Leonard N. Stern School of Business, Department of Economics.
    4. Morey, Edward R. & Thiene, Mara, 2017. "Can Personality Traits Explain Where and With Whom You Recreate? A Latent-Class Site-Choice Model Informed by Estimates From Mixed-Mode LC Cluster Models With Latent-Personality Traits," Ecological Economics, Elsevier, vol. 138(C), pages 223-237.
    5. Barrington-Leigh, C.P., 2024. "The econometrics of happiness: Are we underestimating the returns to education and income?," Journal of Public Economics, Elsevier, vol. 230(C).
    6. Du, Hua & Han, Qi & de Vries, Bauke & Sun, Jun, 2024. "Community solar PV adoption in residential apartment buildings: A case study on influencing factors and incentive measures in Wuhan," Applied Energy, Elsevier, vol. 354(PA).
    7. David Hensher & Andrew Collins & William Greene, 2013. "Accounting for attribute non-attendance and common-metric aggregation in a probabilistic decision process mixed multinomial logit model: a warning on potential confounding," Transportation, Springer, vol. 40(5), pages 1003-1020, September.
    8. Melissa Scharoun-Lee & Penny Gordon-Larsen & Linda Adair & Barry Popkin & Jay Kaufman & Chirayath Suchindran, 2011. "Intergenerational Profiles of Socioeconomic (Dis)advantage and Obesity During the Transition to Adulthood," Demography, Springer;Population Association of America (PAA), vol. 48(2), pages 625-651, May.
    9. 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.
    10. Stefan Boes & Rainer Winkelmann, 2006. "Ordered response models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 167-181, March.
    11. Monia Ranalli & Roberto Rocci, 2024. "Composite likelihood methods for parsimonious model-based clustering of mixed-type 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. 18(2), pages 381-407, June.
    12. Monia Ranalli & Roberto Rocci, 2017. "A Model-Based Approach to Simultaneous Clustering and Dimensional Reduction of Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1007-1034, December.
    13. William Greene, 2014. "Models for ordered choices," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 15, pages 333-362, Edward Elgar Publishing.
    14. Zhang, Q. & Ip, E.H., 2014. "Variable assessment in latent class models," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 146-156.
    15. 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.
    16. Gerhard Tutz & Micha Schneider & Maria Iannario & Domenico Piccolo, 2017. "Mixture models for ordinal responses to account for uncertainty of choice," 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(2), pages 281-305, June.
    17. Silvia Cagnone & Cinzia Viroli, 2018. "Multivariate latent variable transition models of longitudinal mixed data: an analysis on alcohol use disorder," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1399-1418, November.
    18. Damien McParland & Isobel Claire Gormley, 2016. "Model based clustering for mixed data: clustMD," 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. 10(2), pages 155-169, June.

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