Gaussian Predictive Process Models for Large Spatial Data Sets

With scientific data available at geocoded locations, investigators are increasingly turning to spatial process models for carrying out statistical inference. Over the last decade, hierarchical models implemented through Markov chain Monte Carlo methods have become especially popular for spatial modelling, given their flexibility and power to fit models that would be infeasible with classical methods as well as their avoidance of possibly inappropriate asymptotics. However, fitting hierarchical spatial models often involves expensive matrix decompositions whose computational complexity increases in cubic order with the number of spatial locations, rendering such models infeasible for large spatial data sets.…

• NS
  National Science Foundation

  Awards: DEB05-16198, 0706870, DMS-0706870
• NI
  National Institutes of Health

  Awards: 2-R01-ES07750, 1-R01-CA95995