References in zbMATH (referenced in 43 articles )

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  1. Antonio Punzo, Angelo Mazza, Paul D. McNicholas: ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions (2016) arXiv
  2. Galimberti, Giuliano; Scardovi, Elena; Soffritti, Gabriele: Using mixtures in seemingly unrelated linear regression models with non-normal errors (2016)
  3. Kosmidis, Ioannis; Karlis, Dimitris: Model-based clustering using copulas with applications (2016)
  4. 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)
  5. Lin, Tsung-I; McLachlan, Geoffrey J.; Lee, Sharon X.: Extending mixtures of factor models using the restricted multivariate skew-normal distribution (2016)
  6. Malsiner-Walli, Gertraud; Frühwirth-Schnatter, Sylvia; Grün, Bettina: Model-based clustering based on sparse finite Gaussian mixtures (2016)
  7. McNicholas, Paul D.: Model-based clustering (2016)
  8. Bouveyron, C.; Fauvel, M.; Girard, S.: Kernel discriminant analysis and clustering with parsimonious Gaussian process models (2015)
  9. Greselin, Francesca; Ingrassia, Salvatore: Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers (2015)
  10. Ingrassia, Salvatore; Punzo, Antonio; Vittadini, Giorgio; Minotti, Simona C.: Erratum to: “The generalized linear mixed cluster-weighted model” (2015)
  11. Ingrassia, Salvatore; Punzo, Antonio; Vittadini, Giorgio; Minotti, Simona C.: The generalized linear mixed cluster-weighted model (2015)
  12. Lin, Tsung-I; Wu, Pal H.; McLachlan, Geoffrey J.; Lee, Sharon X.: A robust factor analysis model using the restricted skew-$t$ distribution (2015)
  13. Sylla, Seydou N.; Girard, Stéphane; Diongue, Abdou Ka; Diallo, Aldiouma; Sokhna, Cheikh: A classification method for binary predictors combining similarity measures and mixture models (2015)
  14. Andrews, Jeffrey L.; McNicholas, Paul D.: Variable selection for clustering and classification (2014)
  15. Biernacki, Christophe; Lourme, Alexandre: Stable and visualizable Gaussian parsimonious clustering models (2014)
  16. Bouveyron, Charles; Brunet-Saumard, Camille: Discriminative variable selection for clustering with the sparse Fisher-EM algorithm (2014)
  17. Gollini, Isabella; Murphy, Thomas Brendan: Mixture of latent trait analyzers for model-based clustering of categorical data (2014)
  18. Lin, Tsung-I; Ho, Hsiu J.; Lee, Chia-Rong: Flexible mixture modelling using the multivariate skew-$t$-normal distribution (2014)
  19. Lin, Tsung-I; McNicholas, Paul D.; Ho, Hsiu J.: Capturing patterns via parsimonious $t$ mixture models (2014)
  20. McParland, Damien; Gormley, Isobel Claire; McCormick, Tyler H.; Clark, Samuel J.; Kabudula, Chodziwadziwa Whiteson; Collinson, Mark A.: Clustering south african households based on their asset status using latent variable models (2014)

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