Johansen, Adam M., Doucet, Arnaud and Davy, Manuel (2006) Maximum likelihood parameter estimation for latent variable models using sequential Monte Carlo. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse , 2006 May 14-19

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Abstract

We present a sequential Monte Carlo (SMC) method for maximum likelihood (ML) parameter estimation in latent variable models. Standard methods rely on gradient algorithms such as the Expectation- Maximization (EM) algorithm and its Monte Carlo variants. Our approach is different and motivated by similar considerations to simulated annealing (SA); that is we propose to sample from a sequence of artificial distributions whose support concentrates itself on the set of ML estimates. To achieve this we use SMC methods. We conclude by presenting simulation results on a toy problem and a nonlinear non-Gaussian time series model.

Item Type:	Conference Item (Paper)
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Monte Carlo method, Parameter estimation, Latent variables
Book Title:	2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings
Date:	2006
Identification Number:	10.1109/ICASSP.2006.1660735
Status:	Peer Reviewed
Access rights to Published version:	Restricted or Subscription Access
Conference Paper Type:	Paper
Title of Event:	IEEE International Conference on Acoustics, Speech and Signal Processing
Type of Event:	Conference
Location of Event:	Toulouse
Date(s) of Event:	2006 May 14-19
References:	[1] A.P. Dempster, N.M. Laird, and D.B.Rubin, 'Maximum likelihood from incomplete data vie the EM Algorithm' , Journal of the Royal Statistical Society, Series B, vol. 39, pp. 2-38, 1977. [2] C.P. Robert, and G. Casella, Monte Carlo Statistical Methods. New York: Springer-Verlag, second edition,2004. [3] A. Doucet, S.J. Godsill and C.P. Robert , 'Marginal maximum a posteriori estimation using Markov chain Monte Carlo' , Statistics and Computing, vol. 12, pp. 77-84, 2002. [4] E. Jacquier, M. Johannes, and N. Polson , 'MCMC maximum likelihood for latent state models' , Journal of Econometrics, 2005, To Appear. [5] C. Gaetan, and J.F. Yao , 'A multiple-imputation Metropolis version of the EM algorithm' , Biometrika, vol. 90, no. 3, pp. 643-654, 2003 [6] P. Del Moral, Feynman-Kac Formulae. Genealogical and Interacting Particle Approximations, New York: Springer-Verlag, 2004. [7] P. Del Moral, A. Doucet and A. Jasra, 'Sequential Monte Carlo methods for Bayesian computation' (with discussion), in Bayesian Statistics 8, Oxford University Press, to appear 2006. [8] A. Doucet, J.F.G. de Freitas and N.J. Gordon (eds.), Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science, New York: Springer-Verlag, 2001. [9] A. Kong, J.S. Liu, and W.H. Wong, 'Sequential imputations and bayesian missing data problems' . Journal of the American Statistical Association, vol. 89, no. 425, pp. 278-288, March 1994.
URI:	http://wrap.warwick.ac.uk/id/eprint/37289

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