Graphical and Computational Tools to Guide Parameter Choice for the Cluster Weighted Robust Model

DOI10.1080/10618600.2022.2154218OpenAlexW4310524075MaRDI QIDQ6140354

Andrea Cappozzo, Agustín Mayo-Iscar, Luis Angel García-Escudero, Francesca Greselin

Publication date: 22 January 2024

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://hdl.handle.net/11311/1227368

zbMATH Keywords

outliers model-based clustering monitoring robust estimation cluster-weighted modeling eigenvalue constraint trimmed BIC

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

Unnamed Item
Unnamed Item
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Exploring the number of groups in robust model-based clustering
Robust fitting of mixtures using the trimmed likelihood estimator
Constrained parsimonious model-based clustering
Local statistical modeling via a cluster-weighted approach with elliptical distributions
Robust estimation of mixtures of regressions with random covariates, via trimming and constraints
Multivariate linear regression with non-normal errors: a solution based on mixture models
Cluster validation for mixtures of regressions via the total sum of squares decomposition
Cluster-weighted \(t\)-factor analyzers for robust model-based clustering and dimension reduction
Linear grouping using orthogonal regression
A constrained formulation of maximum-likelihood estimation for normal mixture distributions
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Estimating the dimension of a model
Multivariate analysis by data depth: Descriptive statistics, graphics and inference. (With discussions and rejoinder)
Model-based clustering via linear cluster-weighted models
The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers
Breakdown points for maximum likelihood estimators of location-scale mixtures
Clustering and classification via cluster-weighted factor analyzers
Semiautomatic robust regression clustering of international trade data
Model-based clustering with determinant-and-shape constraint
Multivariate response and parsimony for Gaussian cluster-weighted models
Finding the Number of Normal Groups in Model-Based Clustering via Constrained Likelihoods
Model Selection and Model Averaging
Robust Statistics
Avoiding spurious local maximizers in mixture modeling

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