Finding the Number of Normal Groups in Model-Based Clustering via Constrained Likelihoods
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Publication:3391121
DOI10.1080/10618600.2017.1390469OpenAlexW2505884832MaRDI QIDQ3391121
Marco Riani, Andrea Cerioli, Agustín Mayo-Iscar, Luis Angel García-Escudero
Publication date: 28 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: http://uvadoc.uva.es/handle/10324/32023
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Uses Software
Cites Work
- Unnamed Item
- Estimating the Number of Clusters in a Data Set Via the Gap Statistic
- Robust fitting of mixtures using the trimmed likelihood estimator
- A proposal for robust curve clustering
- Robust estimation of the number of components for mixtures of linear regression models
- A fast algorithm for robust constrained clustering
- A general trimming approach to robust cluster analysis
- Bayesian regularization for normal mixture estimation and model-based clustering
- Constrained monotone EM algorithms for finite mixture of multivariate Gaussians
- A constrained formulation of maximum-likelihood estimation for normal mixture distributions
- Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
- Large-sample results for optimization-based clustering methods
- A constrained robust proposal for mixture modeling avoiding spurious solutions
- Simulating mixtures of multivariate data with fixed cluster overlap in FSDA library
- Clustering Criteria and Multivariate Normal Mixtures
- Model-Based Gaussian and Non-Gaussian Clustering
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- Estimating the components of a mixture of normal distributions
- Avoiding spurious local maximizers in mixture modeling
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