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Composite likelihood methods for parsimonious model-based clustering of mixed-type data

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  • Monia Ranalli

    (Sapienza University of Rome)

  • Roberto Rocci

    (Sapienza University of Rome)

Abstract

In this paper, we propose twelve parsimonious models for clustering mixed-type (ordinal and continuous) data. The dependence among the different types of variables is modeled by assuming that ordinal and continuous data follow a multivariate finite mixture of Gaussians, where the ordinal variables are a discretization of some continuous variates of the mixture. The general class of parsimonious models is based on a factor decomposition of the component-specific covariance matrices. Parameter estimation is carried out using a EM-type algorithm based on composite likelihood. The proposal is evaluated through a simulation study and an application to real data.

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  • 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.
    • Handle: RePEc:spr:advdac:v:18:y:2024:i:2:d:10.1007_s11634-023-00539-5
    DOI: 10.1007/s11634-023-00539-5
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