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Non-Stationary Spatial Modeling
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Standard geostatistical models assume stationarity and rely on a variogram model to account for the spatial dependence in the observed data. In some instances, this assumption that the spatial dependence structure is constant throughout the sampling region is clearly violated. We present a spatial model which allows the spatial dependence structure to vary as a function of location. Unlike previous formulations which do not account for uncertainty in the specification of this non-stationarity (eg. Sampson and Guttorp (1992)), we develop a hierarchical model which can incorporate this uncertainty in the resulting inference. The non-stationary spatial dependence is explained through a constructive "process-convolution" approach, which ensures that the resulting covariance structure is valid. We apply this method to an example in toxic waste remediation.
Forward citations
Cited by 2 Pith papers
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Bayesian "Deep" Process Convolutions: An Application in Cosmology
A Bayesian deep process convolution with input-dependent smoothing improves recovery of the nonlinear matter power spectrum from multi-resolution simulations and is released as an R package.
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