A constrained robust proposal for mixture modeling avoiding spurious solutions

DOI10.1007/s11634-013-0153-3zbMath1459.62110OpenAlexW2052840507MaRDI QIDQ2009034

Agustín Mayo-Iscar, Alfonso Gordaliza, Luis Angel García-Escudero

Publication date: 27 November 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/s11634-013-0153-3

zbMATH Keywords

robustnessmaximum likelihood estimationconstraintstrimmingmixture modelseigenvalues restrictions

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Point estimation (62F10) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (13)

The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers ⋮ Robust clustering for functional data based on trimming and constraints ⋮ Eigenvalues and constraints in mixture modeling: geometric and computational issues ⋮ Robust model-based clustering via mixtures of skew-\(t\) distributions with missing information ⋮ Simulating mixtures of multivariate data with fixed cluster overlap in FSDA library ⋮ A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise ⋮ Model-based clustering with determinant-and-shape constraint ⋮ Robust estimation of mixtures of regressions with random covariates, via trimming and constraints ⋮ New distance measures for classifying X-ray astronomy data into stellar classes ⋮ Anomaly and novelty detection for robust semi-supervised learning ⋮ Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates ⋮ Finding the Number of Normal Groups in Model-Based Clustering via Constrained Likelihoods ⋮ Comments on: ``Model-based clustering and classification with non-normal mixture distributions

Uses Software

• TCLUST

Cites Work

• 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
• A general trimming approach to robust cluster analysis
• Trimmed ML estimation of contaminated mixtures
• Constrained monotone EM algorithms for finite mixture of multivariate Gaussians
• Weak convergence and empirical processes. With applications to statistics
• A simulation study to compare robust clustering methods based on mixtures
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Finite mixture models
• Avoiding spurious local maximizers in mixture modeling

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