A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model
type: Item
instance of: scholarly article
title: A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model
zbMATH Open document ID: 1284.62384
author: Isabella Morlini
published in: Advances in Data Analysis and Classification. ADAC
publication date: 2012-11-15T00:00:00Z
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 91C20
zbMATH DE Number: 6106706
zbMATH Keywords: clustering
zbMATH Keywords: E-government
zbMATH Keywords: information and communication technologies
zbMATH Keywords: latent variables
zbMATH Keywords: mixed mode data
zbMATH Keywords: scores estimate
describes a project that uses: MULTIMIX
describes a project that uses: PRELIS
describes a project that uses: Matlab
describes a project that uses: Latent GOLD
MaRDI profile type: Publication
full work available at URL: s11634-011-0101-z
OpenAlex ID: W2066887268
cites work: Model-Based Gaussian and Non-Gaussian Clustering
cites work: Modification indices for the 2-PL and the nominal response model
cites work: Sample Selection Bias as a Specification Error
cites work: Mixture model clustering using the MULTIMIX program
cites work: Identification of Binary Response Models
cites work: Finite mixture models
cites work: Maximum likelihood estimation of the polychoric correlation coefficient
cites work: Analysis of residuals for the multinomial item response model
cites work: Q4821813
DOI: 10.1007/S11634-011-0101-Z
description: scientific article; zbMATH DE number 6106706