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Robust model-based clustering via mixtures of skew-t distributions with missing information

Listed author(s):
  • Wan-Lun Wang

    ()

  • Tsung-I Lin

    ()

Multivariate mixture modeling approach using the skew-t distribution has emerged as a powerful and flexible tool for robust model-based clustering. The occurrence of missing data is a ubiquitous problem in almost every scientific field. In this paper, we offer a computationally flexible EM-type procedure for learning multivariate skew-t mixture models to deal with missing data under missing at random mechanisms. Further, we present an information-based approach to approximating the asymptotic covariance matrix of the maximum likelihood estimators using the outer product of the scores. To assist the development and ease the implementation of our algorithm, two auxiliary permutation matrices are utilized for fast determination of the observed and missing parts of each observation. The practical usefulness of the proposed methodology is illustrated through simulations with varying proportions of artificial missing values and a real data example with genuine missing values. Copyright Springer-Verlag Berlin Heidelberg 2015

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File URL: http://hdl.handle.net/10.1007/s11634-015-0221-y
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Article provided by 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) in its journal Advances in Data Analysis and Classification.

Volume (Year): 9 (2015)
Issue (Month): 4 (December)
Pages: 423-445

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Handle: RePEc:spr:advdac:v:9:y:2015:i:4:p:423-445
DOI: 10.1007/s11634-015-0221-y
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  1. M. C. Jones & M. J. Faddy, 2003. "A skew extension of the "t"-distribution, with applications," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 159-174.
  2. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, October.
  3. ., 1999. "The assessment of capital adequacy," Chapters,in: Handbook of Banking Regulation and Supervision in the United Kingdom, chapter 17 Edward Elgar Publishing.
  4. -, 2003. "Capital flows to Latin America: first quarter 2003," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28822, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
  5. Sharon Lee & Geoffrey McLachlan, 2013. "Model-based clustering and classification with non-normal mixture distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 427-454, November.
  6. Boldea, Otilia & Magnus, Jan R., 2009. "Maximum Likelihood Estimation of the Multivariate Normal Mixture Model," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1539-1549.
  7. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
  8. -, 2003. "Capital flows to Latin America: second quarter 2002," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28812, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
  9. Lounasheimo, Antton, 1999. "The Impact of Human Capital on Economic Growth," Discussion Papers 673, The Research Institute of the Finnish Economy.
  10. Álvarez Alvarado, Marcos Tulio, 2003. "¿Existe una alternativa al capitalismo?," Observatorio de la Economía Latinoamericana, Grupo Eumed.net (Universidad de Málaga), issue 16, November.
  11. Yao, Weixin & Wei, Yan & Yu, Chun, 2014. "Robust mixture regression using the t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 116-127.
  12. -, 2003. "Capital flows to Latin America: first quarter 2002," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28811, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
  13. -, 2003. "Capital flows to Latin America: fourth quarter 2002," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28814, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
  14. Vrbik, Irene & McNicholas, Paul D., 2014. "Parsimonious skew mixture models for model-based clustering and classification," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 196-210.
  15. -, 2003. "Capital flows to Latin America: third quarter 2003," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28824, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
  16. -, 2003. "Capital flows to Latin America: third quarter 2002," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28813, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
  17. Neykov, N. & Filzmoser, P. & Dimova, R. & Neytchev, P., 2007. "Robust fitting of mixtures using the trimmed likelihood estimator," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 299-308, September.
  18. Tsung-I Lin & Hsiu Ho & Pao Shen, 2009. "Computationally efficient learning of multivariate t mixture models with missing information," Computational Statistics, Springer, vol. 24(3), pages 375-392, August.
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