Change of spatiotemporal scale in dynamic models
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DOI: 10.1016/j.csda.2016.02.013
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- Patrick E. Brown & Gareth O. Roberts & Kjetil F. Kåresen & Stefano Tonellato, 2000. "Blur‐generated non‐separable space–time models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 847-860.
- Michael L. Stein, 2005. "Space-Time Covariance Functions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 310-321, March.
- P. G. Blackwell, 2003. "Bayesian inference for Markov processes with diffusion and discrete components," Biometrika, Biometrika Trust, vol. 90(3), pages 613-627, September.
- Wikle C. K. & Milliff R. F. & Nychka D. & Berliner L.M., 2001. "Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 382-397, June.
- Huang, Hsin-Cheng & Cressie, Noel, 1996. "Spatio-temporal prediction of snow water equivalent using the Kalman filter," Computational Statistics & Data Analysis, Elsevier, vol. 22(2), pages 159-175, July.
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Keywords
Bayesian inference; Change of support; Spatio-temporal model; Time-varying parameter model; Upscaling & downscaling method;All these keywords.
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