Estimating space-time covariance functions : a composite likelihood approach ∗

@inproceedings{Bevilacqua2007EstimatingSC,
  title={Estimating space-time covariance functions : a composite likelihood approach ∗},
  author={Maurizio Bevilacqua and Carlo Gaetan and Jorge Mateu and Emilio Porcu},
  year={2007}
}
In the last years there has been a growing interest in the construction space-time covariance functions. However, effective estimation methods for these models are somehow unexplored. In this paper we propose a composite likelihood approach and a weighted variant for the space-time estimation problem. The proposed method can be a valid compromise between the computational burdens, induced by the use of a maximum likelihood approach, and the loss of efficiency induced by using a weighted least… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 18 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 36 references

Model selection for geostatistical models.

Ecological applications : a publication of the Ecological Society of America • 2006
View 4 Excerpts
Highly Influenced

A composite likelihood approach to semivariogram estimation,

F. Curriero, S. Lele
Journal of Agricultural, Biological and Environmental Statistics, • 1999
View 4 Excerpts
Highly Influenced

A composite likelihood approach to binary spatial data,

P. Heagerty, S. Lele
Journal of the American Statistical Association, • 1998
View 7 Excerpts
Highly Influenced

Approximate likelihood for large irregularly spaced spatial data.

Journal of the American Statistical Association • 2007
View 5 Excerpts
Highly Influenced

Pairwise likelihood inference for general state space Models,

C. Varin, P. Vidoni
Econometric Reviews, • 2007
View 1 Excerpt
Highly Influenced

Space-time covariance functions,

M. Stein
Journal of the American Statistical Association, • 2005
View 6 Excerpts
Highly Influenced

Statistical methods for regular monitoring data,

M. Stein
Journal of the Royal Statistical Society B, • 2005
View 6 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…