Monte carlo approximations for general state-space models

Journal of Computational and Graphical Statistics

Volumn 7, Issue 2, 1998, Pages 175-193

  Hürzeler, Markus ^a   Künsch, Hans R ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

Kalman filter; Kalman smoother; Monte Carlo methods; Nonlinear time series analysis; Robustness for time series; State space models

Indexed keywords

EID: 0032221057     PISSN: 10618600     EISSN: 15372715     Source Type: Journal    
DOI: 10.1080/10618600.1998.10474769     Document Type: Article

References (15)

1. Andrade Netto, M. L., Gimeno, L., and Mendes, M. (1978), “On the Optimal and Suboptimal Nonlinear Filtering Problem for Discrete-Time Systems,” IEEE Transactions on Automatic Control, 23, 1062-1067.
2. Carlin, B. P., Poison, N. G., and Staffer, D. S. (1992), “A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling,” Journal of the American Statistical Association, 87, 493-500.
3. Friihwirth-Schnatter, S. (1994), “Data Augmentation and Dynamic Linear Models,” Journal of Time Series Analysis, 15, 183-202.
4. Gordon, N. J., Salmond, D. J., and Smith, A. F. M. (1993), “Novel Approach to Nonlinear/non-Gaussian Bayesian State Estimation,” IEEE Transactions on Radar and Signal Processing, 140, 107-113.
5. Harrison, P. J., and Stevens, C. F. (1976), “Bayesian Forecasting” (with discussion), Journal of the Royal Statistical Society, Ser. B, 38, 205-247.
6. Huber, P. J. (1981), Robust Statistics, New York: Wiley.
7. Kitagawa, G. (1987), “Non-Gaussian State-space Modeling of Nonstationary Time Series” (with discussion), Journal of the American Statistical Association, 82, 1032-1063.
8. Kitagawa, G. (1996), “Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models,” Journal of Computational and Graphical Statistics, 5, 1-25.
9. Martin, R. D. (1981), “Robust Methods for Time Series,” in Applied Time Series II, ed. D. F. Findley, New York: Academic Press, pp. 683-759.
10. Ripley, B. D. (1987), Stochastic Simulation, New York: Wiley.
11. Schick, I. C., and Mitter, S. K. (1994), “Robust Recursive Estimation in the Presence of Heavy-tailed Observation Noise,” The Annals of Statistics, 22, 1045-1080.
12. Shephard, N. (1994), “Partial Non-Gaussian State Space,” Biometrika, 81, 115-131.
13. Silverman, B. W. (1986), Density Estimation for Statistics and Data Analysis, London: Chapman and Hall.
14. Tanizaki, H. (1993), “Nonlinear Filters; Estimation and Applications,” Lecture Notes in Economics and Mathematical Systems, No. 400, Berlin: Springer-Verlag.
15. West, M., Harrison, P. J., and Migon, H. S. (1985), “Dynamic Generalized Linear Models and Bayesian Forecasting” (with discussion), Journal of the American Statistical Association, 80, 73-97.