Free Access
Issue |
ESAIM: PS
Volume 8, August 2004
|
|
---|---|---|
Page(s) | 115 - 131 | |
DOI | https://doi.org/10.1051/ps:2004007 | |
Published online | 15 September 2004 |
- A. Benveniste, M. Métivier and P. Priouret, Adaptive algorithms and stochastic approximations. Springer-Verlag, Berlin (1990). Translated from the French by Stephen S. Wilson. [Google Scholar]
- O. Brandière and M. Duflo, Les algorithmes stochastiques contournent-ils les pièges ? C. R. Acad. Sci. Paris Ser. I Math. 321 (1995) 335–338. [MathSciNet] [Google Scholar]
- H.F. Chen, G. Lei and A.J. Gao, Convergence and robustness of the Robbins-Monro algorithm truncated at randomly varying bounds. Stochastic Process. Appl. 27 (1988) 217–231. [CrossRef] [MathSciNet] [Google Scholar]
- D. Concordet and O.G. Nunez, A simulated pseudo-maximum likelihood estimator for nonlinear mixed models. Comput. Statist. Data Anal. 39 (2002) 187–201. [CrossRef] [MathSciNet] [Google Scholar]
- B. Delyon, M. Lavielle and E. Moulines, Convergence of a stochastic approximation version of the EM algorithm. Ann. Statist. 27 (1999) 94–128. [Google Scholar]
- A.P. Dempster, N.M. Laird and D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39 (1977) 1–38. [Google Scholar]
- M.G. Gu and F.H. Kong, A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems. Proc. Natl. Acad. Sci. USA 95 (1998) 7270–7274 (electronic). [CrossRef] [MathSciNet] [Google Scholar]
- M.G. Gu and H.-T. Zhu, Maximum likelihood estimation for spatial models by Markov chain Monte Carlo stochastic approximation. J. R. Stat. Soc. Ser. B 63 (2001) 339–355. [CrossRef] [MathSciNet] [Google Scholar]
- K. Lange, A gradient algorithm locally equivalent to the EM algorithm. J. R. Stat. Soc. Ser. B 57 (1995) 425–437. [Google Scholar]
- M. Lavielle and E. Lebarbier, An application of MCMC methods to the multiple change-points problem. Signal Processing 81 (2001) 39–53. [Google Scholar]
- M. Lavielle and E. Moulines, A simulated annealing version of the EM algorithm for non-Gaussian deconvolution. Statist. Comput. 7 (1997) 229–236. [CrossRef] [Google Scholar]
- X.-L. Meng and D.B. Rubin, Maximum likelihood estimation via the ECM algorithm: a general framework. Biometrika 80 (1993) 267–278. [CrossRef] [MathSciNet] [Google Scholar]
- K.L. Mengersen and R.L. Tweedie, Rates of convergence of the Hastings and Metropolis algorithms. Ann. Statist. 24 (1996) 101–121. [CrossRef] [MathSciNet] [Google Scholar]
- S.P. Meyn and R.L. Tweedie, Markov chains and stochastic stability, Springer-Verlag London Ltd., London. Comm. Control Engrg. Ser. (1993). [Google Scholar]
- C.-F. Jeff Wu, On the convergence properties of the EM algorithm. Ann. Statist. 11 (1983) 95–103. [Google Scholar]
- J.-F. Yao, On recursive estimation in incomplete data models. Statistics 34 (2000) 27–51 (English). [CrossRef] [MathSciNet] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.