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Rejoinder on: A general science-based framework for dynamical spatio-temporal models

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  • Christopher Wikle
  • Mevin Hooten

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  • Christopher Wikle & Mevin Hooten, 2010. "Rejoinder on: 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 466-468, November.
    • Handle: RePEc:spr:testjl:v:19:y:2010:i:3:p:466-468
    DOI: 10.1007/s11749-010-0214-2
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    Citations

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    Cited by:

    1. Giri Gopalan & Christopher K. Wikle, 2022. "A Higher-Order Singular Value Decomposition Tensor Emulator for Spatiotemporal Simulators," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 22-45, March.
    2. Wilson J. Wright & Peter N. Neitlich & Alyssa E. Shiel & Mevin B. Hooten, 2022. "Mechanistic spatial models for heavy metal pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    3. Kai Yang & Peihua Qiu, 2022. "A three-step local smoothing approach for estimating the mean and covariance functions of spatio-temporal Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(1), pages 49-68, February.
    4. Stanaway, M.A. & Reeves, R. & Mengersen, K.L., 2011. "Hierarchical Bayesian modelling of plant pest invasions with human-mediated dispersal," Ecological Modelling, Elsevier, vol. 222(19), pages 3531-3540.
    5. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    6. Christopher K. Wikle, 2019. "Comparison of Deep Neural Networks and Deep Hierarchical Models for Spatio-Temporal Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 175-203, June.
    7. Huang Huang & Stefano Castruccio & Marc G. Genton, 2022. "Forecasting high‐frequency spatio‐temporal wind power with dimensionally reduced echo state networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 449-466, March.
    8. Dey, Soumen & Moqanaki, Ehsan & Milleret, Cyril & Dupont, Pierre & Tourani, Mahdieh & Bischof, Richard, 2023. "Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects," Ecological Modelling, Elsevier, vol. 479(C).
    9. Margaret R Donald & Kerrie L Mengersen & Rick R Young, 2015. "A Four Dimensional Spatio-Temporal Analysis of an Agricultural Dataset," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    10. Ole F. Christensen, 2012. "Statistics for Spatio-Temporal Data by CRESSIE, N. and WIKLE, C. K," Biometrics, The International Biometric Society, vol. 68(4), pages 1328-1329, December.
    11. Joshua S. North & Christopher K. Wikle & Erin M. Schliep, 2023. "A Review of Data‐Driven Discovery for Dynamic Systems," International Statistical Review, International Statistical Institute, vol. 91(3), pages 464-492, December.
    12. Sondre Hølleland & Hans Arnfinn Karlsen, 2020. "A Stationary Spatio‐Temporal GARCH Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 177-209, March.
    13. Sudipto Banerjee, 2023. "Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” by Huang Huang, Stefano Castruccio, Allison H. Baker and Marc Genton," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 365-369, June.
    14. Robert Richardson & Athanasios Kottas & Bruno Sansó, 2020. "Spatiotemporal modelling using integro‐difference equations with bivariate stable kernels," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1371-1392, December.
    15. Birgit Schrödle & Leonhard Held & Håvard Rue, 2012. "Assessing the Impact of a Movement Network on the Spatiotemporal Spread of Infectious Diseases," Biometrics, The International Biometric Society, vol. 68(3), pages 736-744, September.
    16. 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.
    17. Cécile Hardouin & Noel Cressie, 2018. "Two-scale spatial models for binary data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 1-24, March.
    18. 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.
    19. Ephraim M. Hanks, 2017. "Modeling Spatial Covariance Using the Limiting Distribution of Spatio-Temporal Random Walks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 497-507, April.
    20. Matthew Bonas & Christopher K. Wikle & Stefano Castruccio, 2024. "Calibrated forecasts of quasi‐periodic climate processes with deep echo state networks and penalized quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    21. Philip A. White & Durban G. Keeler & Daniel Sheanshang & Summer Rupper, 2022. "Improving piecewise linear snow density models through hierarchical spatial and orthogonal functional smoothing," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.

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