General location model with factor analyzer covariance matrix structure and its applications

DOI10.1007/s11634-016-0258-6zbMath1414.62205OpenAlexW2484247258MaRDI QIDQ2418329

Mojtaba Khazaei, Leila Amiri, Mojtaba Ganjali

Publication date: 3 June 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-016-0258-6

zbMATH Keywords

classificationcommon factor loadingsfactor analyzergeneral location modelthe EM algorithm

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (3)

Mixtures of general location model with factor analyzer covariance structure for clustering mixed type data ⋮ A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications ⋮ General location multivariate latent variable models for mixed correlated bounded continuous, ordinal, and nominal responses with non-ignorable missing data

Uses Software

• Flury

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Clustering bivariate mixed-type data via the cluster-weighted model
• Model-based clustering, classification, and discriminant analysis of data with mixed type
• Cluster-weighted \(t\)-factor analyzers for robust model-based clustering and dimension reduction
• On the convergence properties of the EM algorithm
• Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients
• Estimating the dimension of a model
• Clustering and classification via cluster-weighted factor analyzers
• A mixture of generalized latent variable models for mixed mode and heterogeneous data
• The generalized linear mixed cluster-weighted model
• Clustering noisy data in a reduced dimension space via multivariate regression trees
• Analysis of Mixed Data
• Latent Variable Models and Factor Analysis
• Mixtures of Continuous and Categorical Variables in Discriminant Analysis: A Hypothesis-Testing Approach
• An Extended General Location Model for Causal Inferences from Data Subject to Noncompliance and Missing Values
• General mixed-data model: Extension of general location and grouped continuous models
• Maximum likelihood estimation for mixed continuous and categorical data with missing values
• Ellipsoidally symmetric extensions of the general location model for mixed categorical and continuous data
• The Grouped Continuous Model for Multivariate Ordered Categorical Variables and Covariate Adjustment
• Classification with discrete and continuous variables via general mixed-data models
• Multivariate Correlation Models with Mixed Discrete and Continuous Variables

This page was built for publication: General location model with factor analyzer covariance matrix structure and its applications