CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST 2008, 24(3) 312-318 DOI:      ISSN: 1001-4268 CN: 31-1256

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 Information and Service This Article Supporting info PDF(216KB) [HTML] Reference Service and feedback Email this article to a colleague Add to Bookshelf Add to Citation Manager Cite This Article Email Alert Keywords Augmentation data Monte Carlo simulation EM algorithm Monte Carlo EM algorithm Newton-Raphson algorithm. Authors Luo Ji PubMed Article by

Acceleration of Monte Carlo EM Algorithm

Luo Ji

School of Finance and Statistics, East China Normal University; School of Mathematics and Statistics, Zhejiang University ofFinance and Economics

Abstract��

EM algorithm is one of the data augmentation algorithms, which usually are used to obtain estimate of the posterior mode of observed data recent years. However, because of its difficulty in calculating the explicit expression of the integral in E step, the application of EM algorithm is limited. While Monte Carlo EM algorithm solves the problem well. Owing to effectively facilitating the integral in E step of EM algorithm by Monte Carlo simulating, Monte Carlo EM algorithm has been successfully used to a wide range of applications. There is, however, the same shortage for EM algorithm and Monte Carlo EM algorithm, that the convergence rate of the two algorithms is linear. So this paper proposes the acceleration of Monte Carlo EM Algorithm, which is based on Monte Carlo EM Algorithm and Newton-Raphson algorithm, to improve the convergence rate. Thus the acceleration of Monte Carlo EM Algorithm has the advantages of both Monte Carlo EM Algorithm and Newton-Raphson algorithm, that is to say it facilitates E step by Monte Carlo simulation and also has quadratic convergence rate in a neighborhood of the posterior mode. Later its excellence in convergence rate is illustrated by a classical example.

Keywords�� Augmentation data   Monte Carlo simulation   EM algorithm   Monte Carlo EM algorithm   Newton-Raphson algorithm.
Received 1900-01-01 Revised 1900-01-01 Online:
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