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Robust mixture modeling based on scale mixtures of skew-normal distributions

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  • Basso, Rodrigo M.
  • Lachos, Víctor H.
  • Cabral, Celso Rômulo Barbosa
  • Ghosh, Pulak

Abstract

A flexible class of probability distributions, convenient for modeling data with skewness behavior, discrepant observations and population heterogeneity is presented. The elements of this family are convex linear combinations of densities that are scale mixtures of skew-normal distributions. An EM-type algorithm for maximum likelihood estimation is developed and the observed information matrix is obtained. These procedures are discussed with emphasis on finite mixtures of skew-normal, skew-t, skew-slash and skew contaminated normal distributions. In order to examine the performance of the proposed methods, some simulation studies are presented to show the advantage of this flexible class in clustering heterogeneous data and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. A real data set is analyzed, illustrating the usefulness of the proposed methodology.

Suggested Citation

  • Basso, Rodrigo M. & Lachos, Víctor H. & Cabral, Celso Rômulo Barbosa & Ghosh, Pulak, 2010. "Robust mixture modeling based on scale mixtures of skew-normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2926-2941, December.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:12:p:2926-2941
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    References listed on IDEAS

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    1. Adelchi Azzalini, 2005. "The Skew-normal Distribution and Related Multivariate Families," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(2), pages 159-188.
    2. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    3. Barry Arnold & Robert Beaver & Richard Groeneveld & William Meeker, 1993. "The nontruncated marginal of a truncated bivariate normal distribution," Psychometrika, Springer;The Psychometric Society, vol. 58(3), pages 471-488, September.
    4. DiCiccio T.J. & Monti A.C., 2004. "Inferential Aspects of the Skew Exponential Power Distribution," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 439-450, January.
    5. Branco, Márcia D. & Dey, Dipak K., 2001. "A General Class of Multivariate Skew-Elliptical Distributions," Journal of Multivariate Analysis, Elsevier, vol. 79(1), pages 99-113, October.
    6. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew "t"-distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389.
    7. Nityasuddhi, Dechavudh & Bohning, Dankmar, 2003. "Asymptotic properties of the EM algorithm estimate for normal mixture models with component specific variances," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 591-601, January.
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    Citations

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    Cited by:

    1. Cabral, Celso Rômulo Barbosa & Lachos, Víctor Hugo & Prates, Marcos O., 2012. "Multivariate mixture modeling using skew-normal independent distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 126-142, January.
    2. repec:eee:jmvana:v:159:y:2017:i:c:p:151-167 is not listed on IDEAS
    3. Bart Keijsers & Bart Diris & Erik Kole, 2015. "Cyclicality in Losses on Bank Loans," Tinbergen Institute Discussion Papers 15-050/III, Tinbergen Institute, revised 01 Sep 2017.
    4. Camila B. Zeller & Celso R. B. Cabral & Víctor H. Lachos, 2016. "Robust mixture regression modeling based on scale mixtures of skew-normal distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 375-396, June.
    5. Melnykov, Volodymyr & Shen, Gang, 2013. "Clustering through empirical likelihood ratio," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 1-10.
    6. Lachos, Victor H. & Bandyopadhyay, Dipankar & Garay, Aldo M., 2011. "Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1208-1217, August.
    7. Tarpey, Thaddeus & Loperfido, Nicola, 2015. "Self-consistency and a generalized principal subspace theorem," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 27-37.
    8. 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).
    9. Wraith, Darren & Forbes, Florence, 2015. "Location and scale mixtures of Gaussians with flexible tail behaviour: Properties, inference and application to multivariate clustering," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 61-73.
    10. Sharon Lee & Geoffrey McLachlan, 2013. "On mixtures of skew normal and skew $$t$$ -distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 241-266, September.

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