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Dynamic models for space-time prediction via Karhunen-Loève expansion

Author

Listed:
  • Lara Fontanella

    (Università degli Studi “G. d'Annunzio”)

  • Luigi Ippoliti

    (Università degli Studi “G. d'Annunzio”)

Abstract

The paper is concerned with the spatio-temporal prediction of spacetime processes. By combining the state-space model with the kriging predictor and Karhunen-Loève Expansion, we present a parsimonious space-time model which is spatially descriptive and temporally dynamic. We consider the difficulties of applying principal component analysis of stochastic processes observed on an irregular network. Using the Voronoi tessellation we make adjustments to the Fredholm integral equation to avoid distorted loading patterns and derive an “adjusted” kriging spatial predictor. This allows for the specification of a space-time model which achieves dimension reduction in the analysis of large spatial and spatio-temporal data sets. As a practical example, the model is applied to study the evolution of the Nitrogen Dioxide (NO2) measurements recorded in the Milan district.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stmapp:v:12:y:2003:i:1:d:10.1007_bf02511584
    DOI: 10.1007/BF02511584
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    References listed on IDEAS

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    Cited by:

    1. Hongxia Wang & Jinde Wang & Bo Huang, 2012. "Prediction for spatio-temporal models with autoregression in errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(1), pages 217-244.
    2. M. Bevilacqua & A. Fassò & C. Gaetan & E. Porcu & D. Velandia, 2016. "Covariance tapering for multivariate Gaussian random fields estimation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 21-37, March.

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