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EM algorithms for multivariate Gaussian mixture models with truncated and censored data

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  • Lee, Gyemin
  • Scott, Clayton
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    Abstract

    We present expectation–maximization (EM) algorithms for fitting multivariate Gaussian mixture models to data that are truncated, censored or truncated and censored. These two types of incomplete measurements are naturally handled together through their relation to the multivariate truncated Gaussian distribution. We illustrate our algorithms on synthetic and flow cytometry data.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0167947312001156
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 9 ()
    Pages: 2816-2829

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    Handle: RePEc:eee:csdana:v:56:y:2012:i:9:p:2816-2829

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Multivariate Gaussian mixture model; EM algorithm; Truncation; Censoring; Multivariate truncated Gaussian distribution;

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    Cited by:
    1. Baran, Sándor, 2014. "Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 227-238.
    2. Jaspers, Stijn & Aerts, Marc & Verbeke, Geert & Beloeil, Pierre-Alexandre, 2014. "A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 30-42.
    3. Bouveyron, Charles & Brunet-Saumard, Camille, 2014. "Model-based clustering of high-dimensional data: A review," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 52-78.

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