We propose a class of semi-constrained models for clustering ordinal and continuous data. Ordinal variables are assumed to be a discretization of some latent continuous variables jointly distribuited with the observed continuous variables as a finite mixture of Gaussians. Parsimonious modeling is obtained by reparameterizing the covariance matrices in terms of factor analysis models semi-constrained across the components. Parameter estimation is carried out using a EM-type algorithm to maximize a composite log-likelihood. The proposal is evaluated through a simulation study and an application to real data.

Semi-constrained model-based clustering of mixed-type data using a composite likelihood approach / Rocci, Roberto; Ranalli, Monia. - (2021), pp. 408-411. (Intervento presentato al convegno Cladag 2021 tenutosi a Firenze).

Semi-constrained model-based clustering of mixed-type data using a composite likelihood approach

Rocci Roberto;Ranalli Monia
2021

Abstract

We propose a class of semi-constrained models for clustering ordinal and continuous data. Ordinal variables are assumed to be a discretization of some latent continuous variables jointly distribuited with the observed continuous variables as a finite mixture of Gaussians. Parsimonious modeling is obtained by reparameterizing the covariance matrices in terms of factor analysis models semi-constrained across the components. Parameter estimation is carried out using a EM-type algorithm to maximize a composite log-likelihood. The proposal is evaluated through a simulation study and an application to real data.
2021
Cladag 2021
mixture models; factor analyzers; composite likelihood; EM algorithm; mixed-type data
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Semi-constrained model-based clustering of mixed-type data using a composite likelihood approach / Rocci, Roberto; Ranalli, Monia. - (2021), pp. 408-411. (Intervento presentato al convegno Cladag 2021 tenutosi a Firenze).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1602385
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