Posterior mode estimation by extended kalman filtering for multivariate dynamic generalized linear models

Journal of the American Statistical Association

Volumn 87, Issue 418, 1992, Pages 501-509

  Fahrmeir, Ludwig ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

Categorical time series; Dynamic generalized linear model; Extended Kalman filtering; Posterior mode estimation

Indexed keywords

EID: 0000350343     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.1992.10475232     Document Type: Article

References (18)

1. Anderson, B. D. D., and Moore, J. B. (1979), Optimal Filtering, Englewood Cliffs, NJ: Prentice-Hall.
2. Baker, R. J., and Thompson, R. (1981), “Composite Link Functions in Generalized Linear Models,” Applied Statistics, 30, 125–131.
3. Fahrmeir, L., and Kaufmann, H. (1985), “Consistency and Asymptotic Normality of the Maximum Likelihood Estimator in Generalized Linear Models,” The Annals of Statistics, 13, 342–368.
4. —— (1987), “Regression Models for Nonstationary Categorical Time Series,” Journal of Time Series Analysis, 8, 147–160.
5. —— (1991), “On Kaiman Filtering, Posterior Mode Estimation and Fisher Scoring in Dynamic Exponential Family Regression,” Metrika, 38, 37–60.
6. Goss, M. (1990), “Schätzung und Identification von Struktur-and Hyper-strukturparametern in Dynamischen Generalisierten Linearen Modellen,” unpublished dissertation, Universität Regensburg, Institut fuer Statistik und Wirtschaftsgeschichte.
7. Hartigan, J. A. (1969), “Linear Bayesian Methods,” Journal of the Royal Statistical Society, Ser. B, 31, 446–454.
8. Harvey, A. C., and Fernandes, C. (1988), “Time Series Models for Count or Qualitative Observations,” Journal of Business & Economic Statistics, 7, 407–422.
9. Kitagawa, G. (1987), “Non-Gaussian State-Space Modeling of Nonstationary Time Series” (With Comments), Journal of the American Statistical Association, 82, 1032–1063.
10. Kaufmann, H. (1987), “Regression Models for Nonstationary Categorical Time Series: Asymptotic Estimation Theory,” The Annals of Statistics, 15, 79–98.
11. Martin, R. D., and Raferty, A. E. (1987), Comment on “Non-Gaussian State-Space Modeling of Nonstationary Time Series,” by G. Kitagawa, Journal of the American Statistical Association, 82, 1044–1050.
12. Nerlove, M. (1983), “Expectations, Plans and Realization in Theory and Practice,” Econometrica, 51, 1251–1279.
13. Sage, A. P., and Melsa, J. L. (1971), Estimation Theory With Applications to Communications and Control, New York: McGraw-Hill.
14. Stiratelli, R., Laird, N., and Ware, J. H. (1984), “Random-Effect Models for Serial Observations With Binary Response,” Biometrics, 40, 961–971.
15. Watson, M. W., and Engle, R. F. (1983), “Alternative Algorithms for the estimation of Dynamic Factor, Mimic and Varying Coefficient Regression Models,” Journal of Econometrics, 23, 385–400.
16. West, M., Harrison, R. J., and Migon, H. S. (1985), “Dynamic Generalized Linear Models and Bayesian Forecasting,” Journal of the American Statistical Association, 80, 73–97.
17. Wong, G. Y., and Mason, W. M. (1985), “The Hierarchical Logistic Regression Model for Multilevel Analysis,” Journal of the American Statistical Association, 80, 513–524.
18. Zehnwirth, B. (1988), “A Generalization of the Kalman Filter for Models With State-Dependent Observation Variance,” Journal of the American Statistical Association, 83, 164–167.