The Kriged Kalman filter
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DOI: 10.1007/BF02565111
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- R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
- Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
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More about this item
Keywords
Bending energy; EM algorithm; Kalman filter; Karahunen-Loeve expansions; Kriging; pollution; spatial temporal modelling; state-space model; 62M10; 62M30; 62M99;All these keywords.
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Statistics
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