A finite mixture model for the unsupervised classification of three-way ordinal data is proposed. Technically, it is a finite mixture of Gaussians observed only through a discretization of its variates. Group specific means and covariances are reparameterized according to parsimonious models. Estimation is carried out through a composite approach to reduce the computational burden.
Model-based simultaneous classification and reduction for three - way ordinal data / Ranalli, Monia; Rocci, Roberto. - (2023), pp. 264-267. (Intervento presentato al convegno ClaDAG2023 tenutosi a Salerno).
Model-based simultaneous classification and reduction for three - way ordinal data
Ranalli Monia;Rocci Roberto
2023
Abstract
A finite mixture model for the unsupervised classification of three-way ordinal data is proposed. Technically, it is a finite mixture of Gaussians observed only through a discretization of its variates. Group specific means and covariances are reparameterized according to parsimonious models. Estimation is carried out through a composite approach to reduce the computational burden.File allegati a questo prodotto
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