TY - JOUR T1 - A Comparison of Spatial Predictors when Datasets Could be Very Large JF - ArXiv Y1 - 2014 A1 - Bradley, J. R. A1 - Cressie, N. A1 - Shi, T. KW - Statistics - Methodology AB -
In this article, we review and compare a number of methods of spatial prediction. To demonstrate the breadth of available choices, we consider both traditional and more-recently-introduced spatial predictors. Specifically, in our exposition we review: traditional stationary kriging, smoothing splines, negative-exponential distance-weighting, Fixed Rank Kriging, modified predictive processes, a stochastic partial differential equation approach, and lattice kriging. This comparison is meant to provide a service to practitioners wishing to decide between spatial predictors. Hence, we provide technical material for the unfamiliar, which includes the definition and motivation for each (deterministic and stochastic) spatial predictor. We use a benchmark dataset of