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

被引：96
|
作者：
Lee, Gyemin [1 ]
Scott, Clayton [1 ,2 ]
机构：
[1] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2012年 / 56卷 / 09期
基金：
美国国家科学基金会;
关键词：
Multivariate Gaussian mixture model; EM algorithm; Truncation; Censoring; Multivariate truncated Gaussian distribution; FLOW-CYTOMETRY DATA; T-PROBABILITIES; NUMERICAL COMPUTATION; MAXIMUM-LIKELIHOOD;
D O I：
10.1016/j.csda.2012.03.003
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：2816 / 2829
页数：14
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