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How to Cite

Nieto, F. H. (1994). Not available. Revista De La Academia Colombiana De Ciencias Exactas, Físicas Y Naturales, 19(72), 117-119. https://doi.org/10.18257/raccefyn.19(72).1994.3796

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Abstract

Sometimes the conventional statistical hypotheses of a state space model for a time series must be replaced for conditional hypotheses on some known information. In this paper, the basic distinction between the two sets of hypotheses is emphasized and the fact that the Kalman Filter equations are valid in both cases is shown.

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References

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Harvey, A. C. (1989) Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press, Cambridge.

Kohn, R. and Ansley, C. F. (1983), Fixed interval estimation in state space models when some of the data are missing or aggregated, Biometrika, Vol. 70, 3, pp 683–8.

Marsaglia, G. (1964), Conditional Means and Covariances of Normal Variables with Singular Covariance Matrix, Journal of the American Statistical Association, 59, pp. 1203–1204.

Nieto, F. H. (1993), Deducción del Filtro de Kalman en el caso de modelos de estados gaussianos singulares, Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales, Vol. XVIII, 71, pp 539–543.

West, M. and Harrison, P. J. (1989), Bayesian Forecasting and Dynamic Models, Springer-Verlag, Berlín.

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