2024-03-29T15:41:48Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/685022023-03-09T11:27:44Zcom_10234_43662com_10234_9col_10234_43643
00925njm 22002777a 4500
dc
Bevilacqua, M.
author
Gaetan, Carlo
author
Mateu, Jorge
author
Porcu, Emilio
author
2012
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 some- how 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 bur- dens, induced by the use of a maximum likelihood approach, and the loss of efficiency induced by using a weighted least squares procedure. An identification criterion based on the composite likelihood is also introduced. The effectiveness of the proposed pro- cedure is illustrated through an extensive simulation experiment, and by reanalising a data set on Irish wind speeds (Haslett and Raftery, 1989). We also address an im- portant issue, which has been recently explored in the literature, on how to select an appropriate space-time model by accounting for the tradeoff between goodness-of-fit and model complexity.
BEVILACQUA, Moreno, et al. Estimating space and space-time covariance functions for large data sets: a weighted composite likelihood approach. Journal of the American Statistical Association, 2012, vol. 107, no 497, p. 268-280
0162-1459
http://hdl.handle.net/10234/68502
http://dx.doi.org/10.1080/01621459.2011.646928
Composite likelihood
Space-time geostatistics
Asymmetry in time
Full symmetry
Irish wind speed data
Separability
Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach