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A general science-based framework for dynamical spatio-temporal models

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  • Christopher Wikle

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  • Mevin Hooten

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Suggested Citation

  • Christopher Wikle & Mevin Hooten, 2010. "A general science-based framework for dynamical spatio-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 417-451, November.
  • Handle: RePEc:spr:testjl:v:19:y:2010:i:3:p:417-451 DOI: 10.1007/s11749-010-0209-z
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    References listed on IDEAS

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    1. 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.
    2. Anders Brix & Peter J. Diggle, 2001. "Spatiotemporal prediction for log-Gaussian Cox processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 823-841.
    3. 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.
    4. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392.
    5. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342.
    6. Borus Jungbacker & Siem Jan Koopman, 2007. "Monte Carlo Estimation for Nonlinear Non-Gaussian State Space Models," Biometrika, Biometrika Trust, vol. 94(4), pages 827-839.
    7. Stroud, Jonathan R. & Stein, Michael L. & Lesht, Barry M. & Schwab, David J. & Beletsky, Dmitry, 2010. "An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 978-990.
    8. Kanti Mardia & Colin Goodall & Edwin Redfern & Francisco Alonso, 1998. "The Kriged Kalman filter," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 7(2), pages 217-282, December.
    9. L. Randall Wray & Stephanie Bell, 2004. "Introduction," Chapters,in: Credit and State Theories of Money, chapter 1 Edward Elgar Publishing.
    10. Michael L. Stein, 2005. "Space-Time Covariance Functions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 310-321, March.
    11. P. Gregori & E. Porcu & J. Mateu & Z. Sasvári, 2008. "On potentially negative space time covariances obtained as sum of products of marginal ones," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 865-882, December.
    12. 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.
    13. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
    14. Philippe Robert-Demontrond & R. Ringoot, 2004. "Introduction," Post-Print halshs-00081823, HAL.
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    1. repec:eee:ecomod:v:222:y:2011:i:19:p:3531-3540 is not listed on IDEAS
    2. Bonneau, Mathieu & Johnson, Fred A. & Romagosa, Christina M., 2016. "Spatially explicit control of invasive species using a reaction–diffusion model," Ecological Modelling, Elsevier, vol. 337(C), pages 15-24.
    3. Al-Sulami, Dawlah & Jiang, Zhenyu & Lu, Zudi & Zhu, Jun, 2017. "Estimation for semiparametric nonlinear regression of irregularly located spatial time-series data," Econometrics and Statistics, Elsevier, vol. 2(C), pages 22-35.

    More about this item

    Keywords

    Bayesian; Hierarchical; Nonlinear; Quadratic; State-space; SST; 35Q62; 37M10; 62F15; 62H11; 62M30; 91B72;

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