NDLI: Acceleration of the EM algorithm using the vector epsilon algorithm

Content Provider	Springer Nature Link
Author	Wang, Mingfeng Kuroda, Masahiro Sakakihara, Michio Geng, Zhi
Copyright Year	2007
Abstract	The expectation–maximization (EM) algorithm is a very general and popular iterative computational algorithm to find maximum likelihood estimates from incomplete data and broadly used to statistical analysis with missing data, because of its stability, flexibility and simplicity. However, it is often criticized that the convergence of the EM algorithm is slow. The various algorithms to accelerate the convergence of the EM algorithm have been proposed. The vector ε algorithm of Wynn (Math Comp 16:301–322, 1962) is used to accelerate the convergence of the EM algorithm in Kuroda and Sakakihara (Comput Stat Data Anal 51:1549–1561, 2006). In this paper, we provide the theoretical evaluation of the convergence of the ε-accelerated EM algorithm. The ε-accelerated EM algorithm does not use the information matrix but only uses the sequence of estimates obtained from iterations of the EM algorithm, and thus it keeps the flexibility and simplicity of the EM algorithm.
Starting Page	469
Ending Page	486
Page Count	18
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	23
Issue Number	3
e-ISSN	16139658
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-10-16
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	The vector ε algorithm The EM algorithm The Louis’s EM algorithm Acceleration of convergence Economic Theory Probability Theory and Stochastic Processes Probability and Statistics in Computer Science Statistics
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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