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

Autor: Monia Ranalli, Roberto Rocci
Rok vydání: 2023
Předmět:
Zdroj: Advances in Data Analysis and Classification.
ISSN: 1862-5355
1862-5347
DOI: 10.1007/s11634-023-00539-5
Popis: 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.
Databáze: OpenAIRE
Externí odkaz: