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Fitting of mixtures with unspecified number of components using cross validation distance estimate

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  • Miloslavsky, Maja
  • van der Laan, Mark J.

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  • Miloslavsky, Maja & van der Laan, Mark J., 2003. "Fitting of mixtures with unspecified number of components using cross validation distance estimate," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 413-428, January.
    • Handle: RePEc:eee:csdana:v:41:y:2003:i:3-4:p:413-428
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    References listed on IDEAS

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    1. Hansen M. H & Yu B., 2001. "Model Selection and the Principle of Minimum Description Length," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 746-774, June.
    2. Feng, Ziding & McCulloch, Charles E., 1992. "Statistical inference using maximum likelihood estimation and the generalized likelihood ratio when the true parameter is on the boundary of the parameter space," Statistics & Probability Letters, Elsevier, vol. 13(4), pages 325-332, March.
    3. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    4. Dankmar Böhning & Ekkehart Dietz & Rainer Schaub & Peter Schlattmann & Bruce Lindsay, 1994. "The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(2), pages 373-388, June.
    5. G. J. McLachlan, 1987. "On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 318-324, November.
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    Cited by:

    1. Tao, Jian & Shi, Ning-Zhong & Lee, S.-Y.Sik-Yum, 2004. "Drug risk assessment with determining the number of sub-populations under finite mixture normal models," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 661-676, July.
    2. Keles Sunduz & van der Laan Mark J. & Dudoit Sandrine & Xing Biao & Eisen Michael B., 2003. "Supervised Detection of Regulatory Motifs in DNA Sequences," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 2(1), pages 1-40, August.
    3. Hunt, Lynette A. & Basford, Kaye E., 2016. "Comparing classical criteria for selecting intra-class correlated features in Multimix," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 350-366.
    4. Bohning, Dankmar & Seidel, Wilfried, 2003. "Editorial: recent developments in mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 349-357, January.
    5. Dankmar Böhning & Ekkehart Dietz & Ronny Kuhnert & Dieter Schön, 2005. "Mixture models for capture-recapture count data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(1), pages 29-43, February.
    6. Proust-Lima, Cécile & Joly, Pierre & Dartigues, Jean-François & Jacqmin-Gadda, Hélène, 2009. "Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1142-1154, February.

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