A note on EM algorithm for mixture models

被引：27
|
作者：
Yao, Weixin [1 ]
机构：
[1] Kansas State Univ, Dept Stat, Manhattan, KS 66506 USA
来源：
STATISTICS & PROBABILITY LETTERS | 2013年 / 83卷 / 02期
关键词：
Adaptive regression; EM algorithm; Edge-preserving smoothers; Mode; Robust regression; LIKELIHOOD; REGRESSION;
D O I：
10.1016/j.spl.2012.10.017
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Expectation-maximization (EM) algorithm has been used to maximize the likelihood function or posterior when the model contains unobserved latent variables. One main important application of EM algorithm is to find the maximum likelihood estimator for mixture models. In this article, we propose an EM type algorithm to maximize a class of mixture type objective functions. In addition, we prove the monotone ascending property of the proposed algorithm and discuss some of its applications. (c) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：519 / 526
页数：8
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