EM algorithms for multivariate Gaussian mixture models with truncated and censored data
AbstractWe 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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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 56 (2012)
Issue (Month): 9 ()
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Web page: http://www.elsevier.com/locate/csda
Multivariate Gaussian mixture model; EM algorithm; Truncation; Censoring; Multivariate truncated Gaussian distribution;
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