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mixsmsn: Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions

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  • Prates, Marcos Oliveira
  • Lachos, Victor Hugo
  • Barbosa Cabral, Celso Rômulo

Abstract

We present the R package mixsmsn, which implements routines for maximum likeli- hood estimation (via an expectation maximization EM-type algorithm) in finite mixture models with components belonging to the class of scale mixtures of the skew-normal distribution, which we call the FMSMSN models. Both univariate and multivariate re- sponses are considered. It is possible to fix the number of components of the mixture to be fitted, but there exists an option that transfers this responsibility to an automated procedure, through the analysis of several models choice criteria. Plotting routines to generate histograms, plug-in densities and contour plots using the fitted models output are also available. The precision of the EM estimates can be evaluated through their esti- mated standard deviations, which can be obtained by the provision of an approximation of the associated information matrix for each particular model in the FMSMSN family. A function to generate artificial samples from several elements of the family is also supplied. Finally, two real data sets are analyzed in order to show the usefulness of the package.

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  • Prates, Marcos Oliveira & Lachos, Victor Hugo & Barbosa Cabral, Celso Rômulo, 2013. "mixsmsn: Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i12).
  • Handle: RePEc:jss:jstsof:v:054:i12
    DOI: http://hdl.handle.net/10.18637/jss.v054.i12
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    2. Morris, Katherine & Punzo, Antonio & McNicholas, Paul D. & Browne, Ryan P., 2019. "Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 145-166.
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    5. Lee, Sharon X. & McLachlan, Geoffrey J., 2022. "An overview of skew distributions in model-based clustering," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    6. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    7. Tiago Dias-Domingues & Helena Mouriño & Nuno Sepúlveda, 2024. "Classification Methods for the Serological Status Based on Mixtures of Skew-Normal and Skew-t Distributions," Mathematics, MDPI, vol. 12(2), pages 1-25, January.
    8. Naderi, Mehrdad & Hung, Wen-Liang & Lin, Tsung-I & Jamalizadeh, Ahad, 2019. "A novel mixture model using the multivariate normal mean–variance mixture of Birnbaum–Saunders distributions and its application to extrasolar planets," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 126-138.
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    16. Libin Jin & Sung Nok Chiu & Jianhua Zhao & Lixing Zhu, 2023. "A constrained maximum likelihood estimation for skew normal mixtures," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(4), pages 391-419, May.
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    18. O’Hagan, Adrian & Murphy, Thomas Brendan & Gormley, Isobel Claire & McNicholas, Paul D. & Karlis, Dimitris, 2016. "Clustering with the multivariate normal inverse Gaussian distribution," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 18-30.

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