Weighted likelihood mixture modeling and model-based clustering

DOI10.1007/s11222-019-09881-1zbMath1436.62255arXiv1811.06899OpenAlexW2962724208WikidataQ127734051 ScholiaQ127734051MaRDI QIDQ2302489

Claudio Agostinelli, Luca Greco

Publication date: 26 February 2020

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1811.06899

zbMATH Keywords

mixtureclassificationrobustnessweighted likelihoodoutlier detectionmultivariate normalEMPearson residuals

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (7)

Robust estimation for multivariate wrapped models ⋮ Robust fitting of mixtures of GLMs by weighted likelihood ⋮ Robust fitting of mixture models using weighted complete estimating equations ⋮ Cluster analysis with cellwise trimming and applications for the robust clustering of curves ⋮ Automatic robust Box-Cox and extended Yeo-Johnson transformations in regression ⋮ Anomaly and novelty detection for robust semi-supervised learning ⋮ Weighted likelihood latent class linear regression

Uses Software

• mclust
• R
• TCLUST
• otrimle
• mtclust

Cites Work

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• Exploring the number of groups in robust model-based clustering
• Robust fitting of mixtures using the trimmed likelihood estimator
• Modelling high-dimensional data by mixtures of factor analyzers
• A fast algorithm for robust constrained clustering
• Error rates for multivariate outlier detection
• S-estimation of hidden Markov models
• Notes on Pearson residuals and weighted likelihood estimating equations
• Finite mixtures of multivariate skew \(t\)-distributions: some recent and new results
• A general trimming approach to robust cluster analysis
• Large-sample results for optimization-based clustering methods
• Robust model selection in regression via weighted likelihood methodology
• Model-based clustering of high-dimensional data: a review
• A reweighting approach to robust clustering
• Minimum disparity estimation for continuous models: Efficiency, distributions and robustness
• A weighted strategy to handle likelihood uncertainty in Bayesian inference
• Weighted likelihood estimation of multivariate location and scatter
• The power of monitoring: how to make the most of a contaminated multivariate sample
• A general set up for minimum disparity estimation
• Weighted likelihood based inference for P(X < Y)
• Weighted Likelihood Equations with Bootstrap Root Search
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Mixture Models, Robustness, and the Weighted Likelihood Methodology
• Multivariate Clustering Procedures with Variable Metrics
• Robust Methods for Data Reduction
• Robust inference for parsimonious model-based clustering
• Multivariate Outlier Detection With High-Breakdown Estimators
• Estimating the components of a mixture of normal distributions
• Avoiding spurious local maximizers in mixture modeling
• Discussion of: ``The power of monitoring: how to make the most of a contaminated multivariate sample
• Model-Based Clustering, Classification, and Density Estimation Using mclust in R

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