Skip to main content

New classes of covariance and spectral density functions for spatio-temporal modelling

Abstract

In the nonseparable spatio-temporal context, several efforts have been made in order to obtain general classes of spatio-temporal covariances. Our aim in this paper is to join several approaches coming from different authors and provide some ideas for the construction of new models of spatio-temporal covariance and spectral density functions. On one hand, we build new covariance families while removing some undesirable features of the previously proposed models, particularly following Stein’s (in J Am Stat Assoc 100:310–321, 2005) remark about Gneiting’s (in J Am Stat Assoc 97:590–600, 2002) approach and about some tensorial product covariance models. We show some of the theoretical results and examples obtained with the product or the sum of spatio-temporal covariance functions or even better with the mixed forms. On the other hand, we define new models for spectral densities through the product of two other spectral densities. We give some characterizations and properties as well as several examples. Finally, we present a practical modelling of Irish wind speed data based on some of the space-time covariance models presented in this paper.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  • Christakos G (2000) Modern spatiotemporal geostatistics. Oxford University Press, New York

    Google Scholar 

  • Christakos G (2002) On a deductive logic-based spatiotemporal random field theory. Probab Theory Math Stat 66:54–65

    Google Scholar 

  • Cressie N, Huang HC (1999) Classes of nonseparable, spatiotemporal stationary covariance functions. J Am Stat Assoc 94:1330–1340

    Article  Google Scholar 

  • De Cesare L, Myers D, Posa D (2001) Product-sum covariance for space-time modeling: an environmental application. Environmetrics 12:11–23

    Article  Google Scholar 

  • Dimitrakopoulos R, Lou X (1994) Spatiotemporal modeling: covariances and ordinary kriging system. In: Dimitrakopoulos R (ed) Geostatistics for the next century. Kluwer, Dordrecht, pp 88–93

    Google Scholar 

  • Egbert GD, Lettenmaier DP (1986) Stochastic modeling of the space-time structure of atmospheric chemical deposition. Water Resour Res 22:165–179

    Article  CAS  Google Scholar 

  • Fernández-Casal R (2003) Geostadistica Espacio-Temporal. Modelos Flexibles de Variogramas Anisotropicos no Separables. Doctoral Thesis, Santiago de Compostela

  • Fuentes M (2002) Spectral methods for nonstationary spatial processes. Biometrika 89:197–210

    Article  Google Scholar 

  • Fuentes M, Smith R (2001) A new class of nonstationary spatial models. Research report of the Statistics Department at North Carolina State University

  • Gneiting T (1997) Normal scale mixtures and dual probability densities. J Stat Comput Simul 9:375–384

    Article  Google Scholar 

  • Gneiting T (2002) Stationary covariance functions for space-time data. J Am Stat Assoc 97:590–600

    Article  Google Scholar 

  • Guttorp P, Sampson PD, Newman K (1992) Non-parametric estimation of spatial covariance with application to monitoring network evaluation. In: Statistics in the environmental and earth sciences. Edward Arnold, London

    Google Scholar 

  • Haslett J, Raftery AE (1989) Space-time modelling with long-memory dependence: assessing ireland’s wind power resource. Appl Stat 38:1–50

    Article  Google Scholar 

  • Hawkins DM, Cressie N (1984) Robust kriging—a proposal. Math Geol 16:3–18

    Article  Google Scholar 

  • Huang HC, Cressie N (1996) Spatio-temporal prediction of snow water equivalent using the Kalman filter. Comput Stat Data Anal 22:159–175

    Article  Google Scholar 

  • Jones RH, Zhang Y (1997) Models for continuous stationary space-time processes. In: Gregoire TG, Brillinger DR, Diggle PJ, Russek-Cohen E, Warren WG, Wolfinger RD (eds) Modelling longitudinal and spatially correlated data. Springer, New York, pp 289–29

    Google Scholar 

  • Kolovos A, Christakos G, Hristopulos DT, Serre ML (2004) Methods for generating non-separable spatiotemporal covariance models with potential environmental applications. Adv Water Resour 27:815–830

    Article  CAS  Google Scholar 

  • Ma C (2003) Families of spatio-temporal stationary covariance models. J Stat Plan Inference 116:489–501

    Article  Google Scholar 

  • Rohuani S, Hall TJ (1989) Space-time kriging of groundwater data. In: Armstrong M (ed) Geostatistics. Kluwer, Dordrecht, vol 2, pp 639–651

  • Sampson PD, Guttorp P (1992) Nonparametric estimation of nonstationary spatial covariance structures. J Am Stat Assoc 87:108–119

    Article  Google Scholar 

  • Solow AR, Gorelik SM (1986) Estimating missing streamflow values by cokriging. Math Geol 18:785–809

    Article  Google Scholar 

  • Stein M (2005) Space time covariance functions. J Am Stat Assoc 100:310–321

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The Editor and referees are acknowledged with thanks. Their precise comments and suggestions have clearly improved an earlier version of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. Mateu.

Additional information

Work partially funded by grant MTM2004-06231 from the Spanish Ministry of Science and Culture.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Porcu, E., Mateu, J. & Saura, F. New classes of covariance and spectral density functions for spatio-temporal modelling. Stoch Environ Res Risk Assess 22 (Suppl 1), 65–79 (2008). https://doi.org/10.1007/s00477-007-0160-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-007-0160-z

Keywords

  • Irish wind speed data
  • Matérn model
  • Mixed-form covariance
  • Nonseparability
  • Product-sum covariance
  • Space-time covariance function
  • Spectral density function