IDEAS home Printed from https://ideas.repec.org/a/mtn/ancoec/2001110.html
   My bibliography  Save this article

On-line spatio-temporal prediction by a state space representation of the generalized space time autoregressive model

Author

Listed:
  • Luigi Ippoliti

Abstract

No abstract is available for this item.

Suggested Citation

  • Luigi Ippoliti, 2001. "On-line spatio-temporal prediction by a state space representation of the generalized space time autoregressive model," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1-2), pages 157-169.
  • Handle: RePEc:mtn:ancoec:2001:110
    as

    Download full text from publisher

    File URL: https://www.dss.uniroma1.it/RePec/mtn/articoli/2001-LIX-1_2-10.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. 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. 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.
    3. Devi Munandar & Budi Nurani Ruchjana & Atje Setiawan Abdullah & Hilman Ferdinandus Pardede, 2023. "Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting," Mathematics, MDPI, vol. 11(13), pages 1-25, July.

    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. 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.
    2. Patrick Vetter & Wolfgang Schmid & Reimund Schwarze, 2016. "Spatio-temporal statistical analysis of the carbon budget of the terrestrial ecosystem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 143-161, March.
    3. 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.
    4. 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.
    5. Sarkka, Aila & Renshaw, Eric, 2006. "The analysis of marked point patterns evolving through space and time," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1698-1718, December.
    6. Alexander Kolovos & George Christakos, 2007. "Stastical Tools in Renewable Energy Modeling: Physical Based, Non-Separable Spatiotemporal Covariance Models," Energy and Environmental Modeling 2007 24000023, EcoMod.
    7. Laurent Bertino & Geir Evensen & Hans Wackernagel, 2003. "Sequential Data Assimilation Techniques in Oceanography," International Statistical Review, International Statistical Institute, vol. 71(2), pages 223-241, August.
    8. 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.
    9. Matthias Katzfuss, 2017. "A Multi-Resolution Approximation for Massive Spatial Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 201-214, January.
    10. Renshaw, Eric & Mateu, Jorge & Saura, Fuensanta, 2007. "Disentangling mark/point interaction in marked-point processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3123-3144, March.
    11. Patrick Vetter & Wolfgang Schmid & Reimund Schwarze, 2016. "Spatio-temporal statistical assessment of anthropogenic CO2 emissions from satellite data," Discussion Paper Series RECAP15 24, RECAP15, European University Viadrina, Frankfurt (Oder).
    12. Gevorgyan Ruben & Melikyan Narine, 2004. "Missing Data Problem and the Empirical Yield Curve Analysis. An Example of T-bills Market in Armenia," EERC Working Paper Series 04-03e, EERC Research Network, Russia and CIS.

    More about this item

    Statistics

    Access and download statistics

    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:mtn:ancoec:2001:110. 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: Marco Alfo' (email available below). General contact details of provider: .

    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.