Parameter Estimation for Discrete-Time Nonlinear Systems Using EM

In this paper we consider parameter estimation of general stochastic nonlinear statespace models using the Maximum Likelihood method. This is accomplished via the employment of an Expectation Maxim ...

[1]  Brett Ninness,et al.  Maximum-likelihood parameter estimation of bilinear systems , 2005, IEEE Transactions on Automatic Control.

[2]  Hisashi Tanizaki,et al.  Computational methods in statistics and econometrics , 2004 .

[3]  Stephen J. Wright,et al.  Numerical Optimization (Springer Series in Operations Research and Financial Engineering) , 2000 .

[4]  A. Doucet,et al.  On-Line Parameter Estimation in General State-Space Models , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[5]  Christophe Andrieu,et al.  Particle methods for change detection, system identification, and control , 2004, Proceedings of the IEEE.

[6]  D. Stoffer,et al.  Parameter estimation in stochastic volatility models with missing data using particle methods and the em algorithm , 2005 .

[7]  Brett Ninness,et al.  Robust maximum-likelihood estimation of multivariable dynamic systems , 2005, Autom..

[8]  Jun S. Liu,et al.  Metropolized independent sampling with comparisons to rejection sampling and importance sampling , 1996, Stat. Comput..

[9]  A. Doucet,et al.  Monte Carlo Smoothing for Nonlinear Time Series , 2004, Journal of the American Statistical Association.

[10]  A. Doucet,et al.  Parameter estimation in general state-space models using particle methods , 2003 .

[11]  Christophe Andrieu,et al.  Online expectation-maximization type algorithms for parameter estimation in general state space models , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[12]  Hisashi Tanizaki.  Nonlinear and Non-Gaussian State Space Modeling Using Sampling Techniques , 2001 .

[13]  Pierre-Arnaud Coquelin,et al.  Numerical methods for sensitivity analysis of Feynman-Kac models , 2007 .

[14]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[15]  Thomas Bo Schön,et al.  Maximum Likelihood Nonlinear System Estimation , 2006 .

[16]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[17]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[18]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[19]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[20]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .