The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers

DOI10.1016/j.csda.2016.01.005zbMath1468.62060OpenAlexW2294162288MaRDI QIDQ1659189

Alfonso Gordaliza, Francesca Greselin, Salvatore Ingrassia, Agustín Mayo-Iscar, Luis Angel García-Escudero

Publication date: 15 August 2018

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

Full work available at URL: http://uvadoc.uva.es/handle/10324/21412

zbMATH Keywords

model-based clusteringrobust estimationmixture modelsconstrained estimationfactor analyzers modeling

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (7)

2nd special issue on robust analysis of complex data ⋮ Assessing trimming methodologies for clustering linear regression data ⋮ Graphical and Computational Tools to Guide Parameter Choice for the Cluster Weighted Robust Model ⋮ A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise ⋮ Detecting Wine Adulterations Employing Robust Mixture of Factor Analyzers ⋮ Robust, fuzzy, and parsimonious clustering, based on mixtures of factor analyzers ⋮ Anomaly and novelty detection for robust semi-supervised learning

Uses Software

• R
• sn
• TCLUST

Cites Work

• Unnamed Item
• Unnamed Item
• Exploring the number of groups in robust model-based clustering
• Robust fitting of mixtures using the trimmed likelihood estimator
• A fast algorithm for robust constrained clustering
• Maximum likelihood estimation of heterogeneous mixtures of Gaussian and uniform distributions
• Flexible mixture modelling using the multivariate skew-\(t\)-normal distribution
• A general trimming approach to robust cluster analysis
• Trimmed ML estimation of contaminated mixtures
• Constrained monotone EM algorithms for finite mixture of multivariate Gaussians
• A constrained formulation of maximum-likelihood estimation for normal mixture distributions
• Generic global identification in factor analysis
• Mixtures of common \(t\)-factor analyzers for modeling high-dimensional data with missing values
• Breakdown points for maximum likelihood estimators of location-scale mixtures
• A constrained robust proposal for mixture modeling avoiding spurious solutions
• Capturing patterns via parsimonious \(t\) mixture models
• A likelihood-based constrained algorithm for multivariate normal mixture models
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• The multivariate skew-normal distribution
• Robust Parameter Estimation in Computer Vision
• Model-Based Classification via Mixtures of Multivariatet-Factor Analyzers
• Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers

This page was built for publication: The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers