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

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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Year of publication:	2012
Authors:	Lee, Gyemin ; Scott, Clayton
Published in:	Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 56.2012, 9, p. 2816-2829
Publisher:	Elsevier
Subject:	Multivariate Gaussian mixture model \| EM algorithm \| Truncation \| Censoring \| Multivariate truncated Gaussian distribution

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010577722