IDEAS home Printed from https://ideas.repec.org/a/spr/testjl/v19y2010i3p417-451.html
   My bibliography  Save this article

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

Author

Listed:
  • Christopher Wikle
  • Mevin Hooten

Abstract

No abstract is available for this item.

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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11749-010-0209-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11749-010-0209-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. Michael L. Stein, 2005. "Space-Time Covariance Functions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 310-321, March.
    4. 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.
    5. 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.
    6. Gneiting T., 2002. "Nonseparable, Stationary Covariance Functions for Space-Time Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 590-600, June.
    7. 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.
    8. Lemos, Ricardo T. & Sansó, Bruno, 2009. "A Spatio-Temporal Model for Mean, Anomaly, and Trend Fields of North Atlantic Sea Surface Temperature," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 5-18.
    9. 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.
    10. 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.
    11. 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, April.
    12. 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, June.
    13. 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.
    14. 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.
    15. Amanda R. Cangelosi & Mevin B. Hooten, 2009. "Models for Bounded Systems with Continuous Dynamics," Biometrics, The International Biometric Society, vol. 65(3), pages 850-856, September.
    16. L. Fahrmeir & H. Kaufmann, 1991. "On kalman filtering, posterior mode estimation and fisher scoring in dynamic exponential family regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 38(1), pages 37-60, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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.
    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. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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).
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guillermo Ferreira & Jorge Mateu & Emilio Porcu, 2018. "Spatio-temporal analysis with short- and long-memory dependence: a state-space approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 221-245, March.
    2. Kim, Yongku & Berliner, L. Mark, 2016. "Change of spatiotemporal scale in dynamic models," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 80-92.
    3. Rutger Jan Lange, 2020. "Bellman filtering for state-space models," Tinbergen Institute Discussion Papers 20-052/III, Tinbergen Institute, revised 19 May 2021.
    4. Fernández-Avilés, G & Montero, JM & Mateu, J, 2011. "Mathematical Genesis of the Spatio-Temporal Covariance Functions," MPRA Paper 35874, University Library of Munich, Germany.
    5. Huang, H.-C. & Martinez, F. & Mateu, J. & Montes, F., 2007. "Model comparison and selection for stationary space-time models," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4577-4596, May.
    6. Fred Espen Benth & Jūratė Šaltytė Benth, 2012. "Modeling and Pricing in Financial Markets for Weather Derivatives," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8457.
    7. S. De Iaco & M. Palma & D. Posa, 2013. "Prediction of particle pollution through spatio-temporal multivariate geostatistical analysis: spatial special issue," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(2), pages 133-150, April.
    8. Leonardo Padilla & Bernado Lagos‐Álvarez & Jorge Mateu & Emilio Porcu, 2020. "Space‐time autoregressive estimation and prediction with missing data based on Kalman filtering," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    9. Jamie L. Cross & Chenghan Hou & Aubrey Poon, 2018. "International Transmission of Macroeconomic Uncertainty in Small Open Economies: An Empirical Approach," Working Papers No 12/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    10. Chan, Joshua C.C., 2013. "Moving average stochastic volatility models with application to inflation forecast," Journal of Econometrics, Elsevier, vol. 176(2), pages 162-172.
    11. Lara Fontanella & Luigi Ippoliti, 2003. "Dynamic models for space-time prediction via Karhunen-Loève expansion," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 61-78, February.
    12. Bretó, Carles, 2014. "On idiosyncratic stochasticity of financial leverage effects," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 20-26.
    13. Lim, S.C. & Teo, L.P., 2009. "Gaussian fields and Gaussian sheets with generalized Cauchy covariance structure," Stochastic Processes and their Applications, Elsevier, vol. 119(4), pages 1325-1356, April.
    14. Bhattacharya, Arnab & Wilson, Simon P., 2018. "Sequential Bayesian inference for static parameters in dynamic state space models," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 187-203.
    15. Matthew J. Heaton & Abhirup Datta & Andrew O. Finley & Reinhard Furrer & Joseph Guinness & Rajarshi Guhaniyogi & Florian Gerber & Robert B. Gramacy & Dorit Hammerling & Matthias Katzfuss & Finn Lindgr, 2019. "A Case Study Competition Among Methods for Analyzing Large Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 398-425, September.
    16. Sameh Abdulah & Yuxiao Li & Jian Cao & Hatem Ltaief & David E. Keyes & Marc G. Genton & Ying Sun, 2023. "Large‐scale environmental data science with ExaGeoStatR," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    17. Kleppe, Tore Selland & Skaug, Hans Julius, 2012. "Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3105-3119.
    18. Salima El Kolei, 2013. "Parametric estimation of hidden stochastic model by contrast minimization and deconvolution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(8), pages 1031-1081, November.
    19. Peter W Gething & Anand P Patil & Simon I Hay, 2010. "Quantifying Aggregated Uncertainty in Plasmodium falciparum Malaria Prevalence and Populations at Risk via Efficient Space-Time Geostatistical Joint Simulation," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-12, April.
    20. Richardson, Robert & Kottas, Athanasios & Sansó, Bruno, 2017. "Flexible integro-difference equation modeling for spatio-temporal data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 182-198.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:testjl:v:19:y:2010:i:3:p:417-451. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.