Department of Biostatistics

This work extends earlier work on spatial meta kriging for the analysis of large multivariatespatial datasets as commonly encountered in environmental and climate sciences. Spatialmeta-kriging partitions the data into subsets, analyzes each subset using a Bayesianspatial process model and then obtains approximate posterior inference for the entiredataset by optimally combining the individual posterior distributions from each subset.Importantly, as is often desired in spatial analysis, spatial meta kriging offers posteriorpredictive inference at arbitrary locations for the outcome as well as the residual spatialsurface after accounting for spatially oriented predictors. Our current work explores spatialmeta kriging idea to enhance scalability of multivariate spatial Gaussian process modelthat uses linear model co-regionalization (LMC) to account for the correlation betweenmultiple components. The approach is simple, intuitive and scales multivariate spatialprocess models to big data effortlessly. A simulation study reveals inferential and predictiveaccuracy offered by spatial meta kriging on multivariate observations.