Estimating a Nonstationary Spatial Structure Using Simulated Annealing

Abstract

During the past decade, a useful model for nonstationary random fields has been developed. This consists of reducing the random field of interest to isotropy via a bijective bi-continuous deformation of the index space. Then the problem consists of estimating this space deformation together with the isotropic correlation in the deformed index space. We propose to estimate both this space deformation and this isotropic correlation using a constrained continuous version of the simulated annealing for a Metropolis-Hastings dynamic. This method provides a nonparametric estimation of the deformation which has the required property to be bijective; so far, the previous nonparametric methods do not guarantee this property. We illustrate our work with two examples, one concerning a precipitation dataset. We also give one idea of how spatial prediction should proceed in the new coordinate space.