mixsmsn

Multivariate mixture modeling using skew-normal independent distributions We consider a flexible class of models, with elements that are finite mixtures of multivariate skew-normal independent distributions. A general EM-type algorithm is employed for iteratively computing parameter estimates and this is discussed with emphasis on finite mixtures of skew-normal, skew-t, skew-slash and skew-contaminated normal distributions. Further, a general information-based method for approximating the asymptotic covariance matrix of the estimates is also presented. The accuracy of the associated estimates and the efficiency of some information criteria are evaluated via simulation studies. Results obtained from the analysis of artificial and real data sets are reported illustrating the usefulness of the proposed methodology. The proposed EM-type algorithm and methods are implemented in the R package mixsmsn. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 23 articles , 1 standard article )

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  1. Lachos, Víctor H.; Moreno, Edgar J.López; Chen, Kun; Cabral, Celso R^omulo Barbosa: Finite mixture modeling of censored data using the multivariate Student-$t$ distribution (2017)
  2. Bansal, Naveen K.; Hamedani, Gholamhossein G.; Maadooliat, Mehdi: Testing multiple hypotheses with skewed alternatives (2016)
  3. 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)
  4. Li, Huiqiong; Wu, Liucang; Yi, Jieyi: A skew-normal mixture of joint location, scale and skewness models (2016)
  5. Zeller, Camila B.; Cabral, Celso R.B.; Lachos, Víctor H.: Robust mixture regression modeling based on scale mixtures of skew-normal distributions (2016)
  6. Browne, Ryan P.; McNicholas, Paul D.: A mixture of generalized hyperbolic distributions (2015)
  7. Sharon X. Lee, Geoffrey J. McLachlan: EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions (2015) arXiv
  8. Tarpey, Thaddeus; Loperfido, Nicola: Self-consistency and a generalized principal subspace theorem (2015)
  9. Cabral, Celso R^omulo Barbosa; Lachos, Víctor Hugo; Zeller, Camila Borelli: Multivariate measurement error models using finite mixtures of skew-Student $t$ distributions (2014)
  10. Lee, Sharon; McLachlan, Geoffrey J.: Finite mixtures of multivariate skew $t$-distributions: some recent and new results (2014)
  11. Lin, Tsung-I; Ho, Hsiu J.; Lee, Chia-Rong: Flexible mixture modelling using the multivariate skew-$t$-normal distribution (2014)
  12. Menardi, Giovanna; Azzalini, Adelchi: An advancement in clustering via nonparametric density estimation (2014)
  13. Ahmad, Ola Suleiman; Pinoli, Jean-Charles: Lipschitz-Killing curvatures of the excursion sets of skew Student’s $t$ random fields (2013)
  14. Galimberti, Giuliano; Montanari, Angela: Discussion of “Model-based clustering and classification with non-normal mixture distributions” by S. X. Lee and G. J. McLachlan (2013)
  15. García-Escudero, L.A.; Gordaliza, A.; Mayo-Iscar, A.: Comments on: “Model-based clustering and classification with non-normal mixture distributions” (2013)
  16. Hennig, Christian: Discussion of “Model-based clustering with non-normal mixture distributions” by S. X. Lee and G. J. McLachlan (2013)
  17. Lee, Sharon X.; McLachlan, Geoffrey J.: On mixtures of skew normal and skew $t$-distributions (2013)
  18. Lee, Sharon X.; McLachlan, Geoffrey J.: Rejoinder to the discussion of “Model-based clustering and classification with non-normal mixture distributions” (2013)
  19. Lee, Sharon X.; McLachlan, Geoffrey J.: Model-based clustering and classification with non-normal mixture distributions (2013)
  20. McNicholas, Paul D.; Browne, Ryan P.; Murray, Paula M.: Discussion of `Model-based clustering and classification with non-normal mixture distributions’ by Lee and McLachlan (2013)

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