Finding the Number of Normal Groups in Model-Based Clustering via Constrained Likelihoods
Author:
Affiliation:
1. Dipart. di Scienze Economiche e Aziendali, Università di Parma, Parma, Italy
2. Dpto. de Estadística e I.O. and IMUVA, Universidad de Valladolid, Valladolid, Spain
Funder
Spanish Ministerio de Economía y Competitividad
Consejería de Educación de la Junta de Castilla y León and FEDER
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/10618600.2017.1390469
Reference30 articles.
1. Model-Based Gaussian and Non-Gaussian Clustering
2. Assessing a mixture model for clustering with the integrated completed likelihood
3. Large-sample results for optimization-based clustering methods
4. Gaussian parsimonious clustering models
5. Estimating the components of a mixture of normal distributions
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