Penalized factor mixture analysis for variable selection in clustered data

DOI10.1016/j.csda.2009.05.025zbMath1453.62094OpenAlexW1997069007MaRDI QIDQ961929

Cinzia Viroli, Angela Montanari, Giuliano Galimberti

Publication date: 1 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2009.05.025

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Model-based clustering of high-dimensional data: a review ⋮ Model-based clustering ⋮ Variable selection in model-based clustering and discriminant analysis with a regularization approach ⋮ Clustered sparse structural equation modeling for heterogeneous data ⋮ Modelling the role of variables in model-based cluster analysis ⋮ Bayesian variable selection and model averaging in the arbitrage pricing theory model ⋮ Prediction with a flexible finite mixture-of-regressions ⋮ Variable selection methods for model-based clustering ⋮ Discriminative variable selection for clustering with the sparse Fisher-EM algorithm ⋮ Inferring food intake from multiple biomarkers using a latent variable model ⋮ Italian contributions on some recent research topics in cluster analysis ⋮ Using conditional independence for parsimonious model-based Gaussian clustering ⋮ Mixtures of Gaussian copula factor analyzers for clustering high dimensional data

Uses Software

• mclust
• R
• PGMM
• ArrayCluster

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Modelling high-dimensional data by mixtures of factor analyzers
• Dimensionally reduced mixtures of regression models
• Estimating the dimension of a model
• Enhanced model-based clustering, density estimation, and discriminant analysis software:\newline MCLUST
• MCLUST: Software for model-based cluster analysis
• Variable selection using MM algorithms
• Subset Clustering of Binary Sequences, with an Application to Genomic Abnormality Data
• Variable Selection in Finite Mixture of Regression Models
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model-Based Gaussian and Non-Gaussian Clustering
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Variable Selection for Model-Based Clustering