Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field

By R. Ingebrigtsen, Finn Lindgren, Ingelin Steinsland, Sara Martino. 2014

47 citations recorded by the source corpus.

Estimation of stationary dependence structure parameters using only a single realisation of the spatial process, typically leads to inaccurate estimates and poorly identified parameters. A common way to handle this is to fix some of the parameters, or within the Bayesian framework, impose prior knowledge. In many applied settings, stationary models are not flexible enough to model the process of interest, thus non-stationary spatial models are used. However, more flexible models usually means more parameters, and the identifiability problem becomes even more challenging. We investigate aspects of estimation of a Bayesian non-stationary spatial model for annual precipitation using observations from multiple years. The model contains replicates of the spatial field, which increases precision of the estimates and makes them less prior sensitive. Using R-INLA, we analyse precipitation data from southern Norway, and investigate statistical properties of the replicate model in a simulation study. The non-stationary spatial model we explore belongs to a recently introduced class of stochastic partial differential equation (SPDE) based spatial models. This model class allows for non-stationary models with explanatory variables in the dependence structure. We derive conditions to facilitate prior specification for these types of non-stationary spatial models.

Subfields: Economics and Econometrics, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/74662411520399049199f2ce0429a678018b090b

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Citations in the corpus

Second-order non-stationary modeling approaches for univariate geostatistical data (2017, methodology): “Iovleff and Perrin (2004) implement a simulated annealing algorithm with constraints for the fitting of space deformation model.”
Constructing Priors that Penalize the Complexity of Gaussian Random Fields (2015, methodology): “The dataset was previously used by Ingebrigtsen, Lindgren, and Steinsland and Ingebrigtsen et al. (2015) to study the use of elevation as a covariate in the covariance structure and associated priors.”
Spatial modelling with R-INLA: A review (2018, methodology): “Ingebrigtsen et al. (2015) investigate estimation of models with covariates in both the mean structure and the covariance structure using multiple realizations.”