Model-based linear clustering

DOI10.1002/cjs.10082zbMath1349.62288OpenAlexW2051644092MaRDI QIDQ3086523

Guohua Yan, William J. Welch, Ruben H. Zamar

Publication date: 30 March 2011

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.10082

zbMATH Keywords

EM algorithmmixture modelprofile likelihooderrors-in-variables modelorthogonal regressionlinear cluster

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05)

Related Items (1)

The next‐generation K‐means algorithm

Uses Software

• mclust
• mixreg
• Algorithm 39

Cites Work

• Unnamed Item
• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• Linear grouping using orthogonal regression
• A constrained formulation of maximum-likelihood estimation for normal mixture distributions
• An entropy criterion for assessing the number of clusters in a mixture model
• Note on the consistency of the maximum likelihood estimate for nonidentifiable distributions
• Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters
• 389: Separating Mixtures of Normal Distributions
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• An improvement of the NEC criterion for assessing the number of clusters in a mixture model
• Model-Based Gaussian and Non-Gaussian Clustering
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Estimating the Propagation Rate of a Viral Infection of Potato Plants via Mixtures of Regressions
• Finite mixture models
• Note on the Consistency of the Maximum Likelihood Estimate

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