On Markov Chain Monte Carlo Methods For Nonlinear And Non-Gaussian State-Space Models (1999)
| Venue: | Communications in Statistics, Simulation and Computation, Vol.28, No.4, pp.867 |
| Citations: | 6 - 2 self |
BibTeX
@INPROCEEDINGS{Geweke99onmarkov,
author = {John Geweke and H. Tanizaki},
title = {On Markov Chain Monte Carlo Methods For Nonlinear And Non-Gaussian State-Space Models},
booktitle = {Communications in Statistics, Simulation and Computation, Vol.28, No.4, pp.867},
year = {1999},
pages = {89--4}
}
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Abstract
In this paper, a nonlinear and/or non-Gaussian smoother utilizing Markov chain Monte Carlo Methods is proposed, where the measurement and transition equations are specified in any general formulation and the error terms in the state-space model are not necessarily normal. The random draws are directly generated from the smoothing densities. For random number generation, the Metropolis-Hastings algorithm and the Gibbs sampling technique are utilized. The proposed procedure is very simple and easy for programming, compared with the existing nonlinear and non-Gaussian smoothing techniques. Moreover, taking several candidates of the proposal density function, we examine precision of the proposed estimator.
