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Computationally efficient learning of multivariate t mixture models with missing information


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  • Tsung-I Lin


  • Hsiu Ho
  • Pao Shen
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    Bibliographic Info

    Article provided by Springer in its journal Computational Statistics.

    Volume (Year): 24 (2009)
    Issue (Month): 3 (August)
    Pages: 375-392

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    Handle: RePEc:spr:compst:v:24:y:2009:i:3:p:375-392

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    Keywords: Classifier; Learning with missing information; Multivariate t mixture models; PX-EM algorithm; Outlying observations;


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    1. S. P. Brooks & P. Giudici & G. O. Roberts, 2003. "Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 3-39.
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    Cited by:
    1. Lin, Tsung-I, 2014. "Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 183-195.
    2. Lin, Tsung-I & McNicholas, Paul D. & Ho, Hsiu J., 2014. "Capturing patterns via parsimonious t mixture models," Statistics & Probability Letters, Elsevier, vol. 88(C), pages 80-87.
    3. Zhao, Jianhua & Shi, Lei, 2014. "Automated learning of factor analysis with complete and incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 205-218.
    4. Wan-Lun Wang & Tsung-I Lin, 2013. "An efficient ECM algorithm for maximum likelihood estimation in mixtures of t-factor analyzers," Computational Statistics, Springer, vol. 28(2), pages 751-769, April.
    5. Tzy-Chy Lin & Tsung-I Lin, 2010. "Supervised learning of multivariate skew normal mixture models with missing information," Computational Statistics, Springer, vol. 25(2), pages 183-201, June.
    6. Paul McNicholas & Ryan Browne & Paula Murray, 2013. "Discussion of ‘Model-based clustering and classification with non-normal mixture distributions’ by Lee and McLachlan," Statistical Methods and Applications, Springer, vol. 22(4), pages 467-472, November.


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