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| HAL : hal-00515832, version 2 |
| Fiche détaillée |  Récupérer au format |
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| Versions disponibles : | v1 (08-09-2010) | v2 (16-11-2010) |
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| A fast, near efficient, randomized-trace based method for fitting stationary Gaussian spatial models to large noisy data sets in the case of a single range-parameter |
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| Didier A. Girard 1 |
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| (12/11/2010) |
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| We consider the inference problem of fitting to noisy (gridded) observations, an isotropic zero-mean stationary Gaussian field model which belongs to the Matern family with known regularity index $\nu \geq 0$, or to the spherical family. For estimating the correlation range and the variance of the field, two simple estimating functions based on the so-called ``conditional Gibbs energy mean'' (CGEM) and the empirical variance (EV) were recently introduced. This article presents a rather extensive Monte Carlo simulation study for problems with around a thousand observations and settings including large, moderate, and even ``small'', correlation ranges. It empirically demonstrates that the statistical efficiency of CGEM-EV is quite satisfying provided the signal-to-noise ratio is strong enough or $\nu$ is not too large. |
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| 1 : | Laboratoire Jean Kuntzmann (LJK) |
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CNRS : UMR5224 – Université Joseph Fourier - Grenoble I – Université Pierre Mendès-France - Grenoble II – Institut Polytechnique de Grenoble |
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| Domaine | : | Mathématiques/Statistiques Statistiques/Théorie |
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| hal-00515832, version 2 | |
| http://hal.archives-ouvertes.fr/hal-00515832 | |
| oai:hal.archives-ouvertes.fr:hal-00515832 | |
| Contributeur : Didier A. Girard | |
| Soumis le : Mardi 16 Novembre 2010, 12:17:41 | |
| Dernière modification le : Mardi 23 Novembre 2010, 19:10:43 | |
