On computing the expected fisher information matrix for state-space model parameters

Statistics and Probability Letters

Volumn 26, Issue 4, 1996, Pages 347-355

  Cavanaugh, Joseph E ^a   Shumway, Robert H ^b  

^a UNIVERSITY OF MISSOURI   (United States)

^b NONE

Author keywords

EM algorithm; Kalman filter; Recursive algorithm; Time series

Indexed keywords

EID: 0030099661     PISSN: 01677152     EISSN: None     Source Type: Journal    
DOI: 10.1016/0167-7152(95)00031-3     Document Type: Article

References (9)

1. Brockwell, P.J. and R.A. Davis (1991), Time Series: Theory and Methods (Springer, New York, 2nd ed.),
2. Cavanaugh, J.E. (1993), Small-sample model selection in the general state-space setting, Ph.D. Dissertation, University of California, Davis, Division of Statistics (Printed by UMI Dissertation Services, Ann Arbor, MI).
3. Harvey, A.C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter (Cambridge Univ. Press, New York).
4. Kalman, R.E. (1960), A new approach to linear filtering and prediction problems, Trans. ASME J. Basic Engng. 82, 35-45.
5. Kalman, R.E. and R.S. Bucy (1961), New results in linear filtering and prediction theory, Trans. ASME J. Basic Engng. 83, 95-108.
6. Schweppe, F. (1965). Evaluation of likelihood functions for Gaussian signals, IEEE Trans. Inform. Theory 11, 61-70.
7. Shumway, R.H. (1988), Applied Statistical Time Series Analysis (Prentice-Hall, Englewood Cliffs, NJ).
8. Shumway, R.H. and D.S. Stoffer (1982), An approach to time series smoothing and forecasting using the EM algorithm, J. Time Series Anal. 3, 253-264.
9. Watanabe, N. (1985), Note on the Kalman filter with estimated parameters, J. Time Series Anal. 6, 269-278.