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Conditional simulation in dynamic linear models for spatial and temporal predictions of diffusive phenomena

Author

Listed:
  • Tonio Di Battista

    (Universitá degli Studi “G. d’Annunzio”)

  • Lara Fontanella

    (Universitá degli Studi “G. d’Annunzio”)

  • Luigi Ippoliti

    (Universitá degli Studi “G. d’Annunzio”)

Abstract

. A spatial time series framework is used for stochastic modelling of daily average Sulphur Dioxide (SO2) levels in the Milan district. Within a spatio-temporal Kalman filter algorithm, stochastic conditional simulation is performed to obtain spatial and temporal predictions of the observed process. Unlike other recent space-time Kalman filters, the inclusion of a point source trend model also allows the development of a spatio-temporal state-space model that achieves dimension reduction in the analysis of large data set.

Suggested Citation

  • Tonio Di Battista & Lara Fontanella & Luigi Ippoliti, 2004. "Conditional simulation in dynamic linear models for spatial and temporal predictions of diffusive phenomena," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(3), pages 361-375, February.
    • Handle: RePEc:spr:stmapp:v:12:y:2004:i:3:d:10.1007_s10260-003-0065-z
    DOI: 10.1007/s10260-003-0065-z
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