AMS2006-8: Bayesian treed Gaussian process models

Robert B. Gramacy and Herbert K. H. Lee
12/31/2006 09:00 AM
Applied Mathematics & Statistics
This paper explores nonparametric and semiparametric nonstationary modeling methodologies that couple stationary Gaussian processes and (limiting) linear models with treed partitioning. Partitioning is a simple but effective method for dealing with nonstationarity. Mixing between full Gaussian processes and simple linear models can yield a more parsimonious spatial model while significantly reducing computational effort. The methodological developments and statistical computing details which make this approach efficient are described in detail. Illustrations of our model are given for both synthetic and real datasets.