The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Coupling a stochastic approximation version of EM with an MCMC procedure
Open accessAbstract
The stochastic approximation version of EM (SAEM) proposed by Delyon et al. (1999) is a powerful alternative to EM when the E-step is intractable. Convergence of SAEM toward a maximum of the observed likelihood is established when the unobserved data are simulated at each iteration under the conditional distribution. We show that this very restrictive assumption can be weakened. Indeed, the results of Benveniste et al. for stochastic approximation with Markovian perturbations are used to establish the convergence of SAEM when it is coupled with a Markov chain Monte-Carlo procedure. This result is very useful for many practical applications. Applications to the convolution model and the change-points model are presented to illustrate the proposed method.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Markov Chains and Stochastic Stability
Meyn, S. P., & Tweedie, R. L. (1993) · Springer eBooks · doi:10.1007/978-1-4471-3267-7 · Cited by 5,041
On the Convergence Properties of the EM Algorithm
Wu, C. F. J. (1983) · The Annals of Statistics · doi:10.1214/aos/1176346060 · Cited by 3,269
Adaptive Algorithms and Stochastic Approximations
Benveniste, A., Métivier, M., & Priouret, P. (1990) · Springer eBooks · doi:10.1007/978-3-642-75894-2 · Cited by 2,006
Maximum likelihood estimation via the ECM algorithm: A general framework
MENG, X.-L., & RUBIN, D. B. (1993) · Biometrika · doi:10.1093/biomet/80.2.267 · Cited by 1,861
Convergence of a stochastic approximation version of the EM algorithm
Delyon, B., Lavielle, M., & Moulines, E. (1999) · The Annals of Statistics · doi:10.1214/aos/1018031103 · Cited by 778
Self-citationRates of convergence of the Hastings and Metropolis algorithms
Mengersen, K. L., & Tweedie, R. L. (1996) · The Annals of Statistics · doi:10.1214/aos/1033066201 · Cited by 553
A Gradient Algorithm Locally Equivalent to the Em Algorithm
Lange, K. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02037.x · Cited by 376
Convergence and robustness of the Robbins-Monro algorithm truncated at randomly varying bounds
Chen, H.-F., Guo, L., & Gao, A. (1987) · Stochastic Processes and their Applications · doi:10.1016/0304-4149(87)90039-1 · Cited by 107
Gu, M. G., & Hongtu Zhu, B. (2001) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00289 · Cited by 101
Cited by
Kurzrock, R., Goel, S., Wheler, J., Hong, D., Fu, S., Rezai, K., Morgan‐Linnell, S. K., Urien, S., Mani, S., Chaudhary, I., et al. (2012) · Cancer · doi:10.1002/cncr.27647 · Cited by 50
Metadata
- Authors
- Kuhn, E., & Lavielle, M.
- Venue
- ESAIM: Probability and Statistics
- Publisher
- EDP Sciences
- Published
- 2004-08
- Volume
- 8
- Pages
- 115-131
- ISSN
- 1262-3318 (ISSN-L)
- 1292-8100
- OpenAlex
- W2055742299
- Cited by
- 262
- References
- 14
- FWCI
- 7.22
- Type
- journal article
- Language
- en
- Open access
- bronze
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