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

作     者：Ranalli, Monia Rocci, Roberto 

作者机构：Sapienza Univ Rome Piazzale Aldo Moro 5 Rome Italy 

出 版 物：《ADVANCES IN DATA ANALYSIS AND CLASSIFICATION》 (数据分析与分类进展)

年 卷 期：2024年第18卷第2期

页      面：381-407页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：We would like to thank the three reviewers and the editor for their helpful comments and suggestions. We believe that they helped us to improve the quality of the manuscript 

主　　题：Mixture models Factor analyzers Composite Likelihood EM algorithm Mixed-type data 

摘      要：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.