Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
416410 | Computational Statistics & Data Analysis | 2012 | 14 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Gyemin Lee, Clayton Scott,