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

Authors:
Gyemin Lee;Clayton Scott
Affiliations:
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA;Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA and Department of Statistics, University of Michigan, Ann Arbor, MI, 48109, USA
Venue:
Computational Statistics & Data Analysis
Year:
2012

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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.