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Optimization via simulation is a promising approach for solving maximum likelihood problems in incomplete data models. Among the techniques proposed to date, the Monte-Carlo EM algorithm (MCEM) proposed by Wei and Tanner (1991) has a strong potential but very little is known on its behavior and on strategies for monitoring its convergence. In this contribution, the convergence of MCEM is investigated with a particular emphasis on the stability issue (which is not guaranteed in the original algorithm described by Wei and Tanner). A random truncation strategy, inspired by the Chen's truncation method for stochastic approximation algorithms, is proposed and analyzed. Finally, the application of our results to blind estimation problems in which the complete data likelihood is from the exponential family is discussed
Published in:
Statistical Signal and Array Processing, 1998. Proceedings., Ninth IEEE SP Workshop on
Date of Conference: 14-16 Sep 1998
- Page(s):
- 80 - 83
- Meeting Date :
- 14 Sep 1998-16 Sep 1998
- Print ISBN:
- 0-7803-5010-3
- INSPEC Accession Number:
- 6232589
- Conference Location :
- Portland, OR
- Digital Object Identifier :
- 10.1109/SSAP.1998.739339
- Product Type:
- Conference Publications
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