References in zbMATH (referenced in 56 articles )

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  1. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  2. Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
  3. Luca Scrucca; Adrian Raftery: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R (2018)
  4. Papastamoulis, Panagiotis: Overfitting Bayesian mixtures of factor analyzers with an unknown number of components (2018)
  5. Dang, Utkarsh J.; Punzo, Antonio; McNicholas, Paul D.; Ingrassia, Salvatore; Browne, Ryan P.: Multivariate response and parsimony for Gaussian cluster-weighted models (2017)
  6. Hui, Francis K. C.: Model-based simultaneous clustering and ordination of multivariate abundance data in ecology (2017)
  7. Murray, Paula M.; Browne, Ryan P.; McNicholas, Paul D.: Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering (2017)
  8. Naderi, Mehrdad; Arabpour, Alireza; Lin, Tsung-I; Jamalizadeh, Ahad: Nonlinear regression models based on the normal mean-variance mixture of Birnbaum-Saunders distribution (2017)
  9. Punzo, Antonio; McNicholas, Paul. D.: Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model (2017)
  10. Ranalli, Monia; Rocci, Roberto: A model-based approach to simultaneous clustering and dimensional reduction of ordinal data (2017)
  11. Wang, Wan-Lun; Liu, Min; Lin, Tsung-I: Robust skew-$t$ factor analysis models for handling missing data (2017)
  12. Antonio Punzo, Angelo Mazza, Paul D. McNicholas: ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions (2016) arXiv
  13. Berta, Paolo; Ingrassia, Salvatore; Punzo, Antonio; Vittadini, Giorgio: Multilevel cluster-weighted models for the evaluation of hospitals (2016)
  14. Galimberti, Giuliano; Scardovi, Elena; Soffritti, Gabriele: Using mixtures in seemingly unrelated linear regression models with non-normal errors (2016)
  15. Kosmidis, Ioannis; Karlis, Dimitris: Model-based clustering using copulas with applications (2016)
  16. Lee, Sharon X.; McLachlan, Geoffrey J.: Finite mixtures of canonical fundamental skew $t$-distributions. The unification of the restricted and unrestricted skew $t$-mixture models (2016)
  17. Lin, Tsung-I; McLachlan, Geoffrey J.; Lee, Sharon X.: Extending mixtures of factor models using the restricted multivariate skew-normal distribution (2016)
  18. Malsiner-Walli, Gertraud; Frühwirth-Schnatter, Sylvia; Grün, Bettina: Model-based clustering based on sparse finite Gaussian mixtures (2016)
  19. McNicholas, Paul D.: Model-based clustering (2016)
  20. Morris, Katherine; McNicholas, Paul D.: Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures (2016)

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