Computationally efficient learning of multivariate t mixture models with missing information
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Bibliographic InfoArticle provided by Springer in its journal Computational Statistics.
Volume (Year): 24 (2009)
Issue (Month): 3 (August)
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Web page: http://www.springerlink.com/link.asp?id=120306
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- 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.
- 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.
- 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.
- 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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