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Polynomial nonlinear spatio‐temporal integro‐difference equation models

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  • Christopher K. Wikle
  • Scott H. Holan

Abstract

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  • Christopher K. Wikle & Scott H. Holan, 2011. "Polynomial nonlinear spatio‐temporal integro‐difference equation models," Journal of Time Series Analysis, Wiley Blackwell, vol. 32, pages 339-350, July.
    • Handle: RePEc:bla:jtsera:v:32:y:2011:i::p:339-350
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    Citations

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    Cited by:

    1. Jonathan R. Bradley & Christopher K. Wikle & Scott H. Holan, 2016. "Bayesian Spatial Change of Support for Count-Valued Survey Data With Application to the American Community Survey," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 472-487, April.
    2. Joshua S. North & Christopher K. Wikle & Erin M. Schliep, 2022. "A Bayesian Approach for Data-Driven Dynamic Equation Discovery," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 728-747, December.
    3. 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.
    4. 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.
    5. 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.
    6. 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.

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