Acceleration of the EM algorithm using the vector epsilon algorithm

作者：Wang Mingfeng; Kuroda Masahiro^*; Sakakihara Michio; Geng Zhi

来源：Computational Statistics, 2008, 23(3): 469-486.

DOI：10.1007/s00180-007-0089-1

• the vector epsilon algorithm
• the EM algorithm
• the Louis's EM algorithm
• acceleration of convergence

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 epsilon 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 epsilon-accelerated EM algorithm. The epsilon-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.

• 出版日期2008-7
• 单位北京大学