A1400
Title: A method to incorporate subsampling into Bayesian models for high-dimensional spatial data
Authors: Jonathan Bradley - Florida State University (United States) [presenting]
Abstract: Spatial statistical models with weakly stationary process assumptions have become standard in spatial statistics. However, one disadvantage of such models is the computation time, which rapidly increases with the number of data points. The goal is to apply an existing subsampling strategy to standard spatial additive models and to derive the spatial statistical properties. The approach has the advantage that one does not require any additional restrictive model assumptions. That is, computational gains increase as model assumptions are removed when using the model framework. This provides one solution to the computational bottlenecks that occur when applying methods such as Kriging to big data. Several properties of this new approach are provided in terms of moments, sill, nugget, and range under several sampling designs. An advantage of the approach is that it subsamples without throwing away data, and can be implemented using datasets of any size that can be stored. The results of the spatial data subset model approach are presented on simulated datasets and on a large dataset consisting of 150,000 observations of daytime land surface temperatures measured by the MODIS instrument onboard the Terra satellite.
