Inference and prediction in multiscale data models

Thomas M. Menino Convention & Exhibition Center 

We consider statistically principled inference and prediction in some models that are routinely used in the context of multiscale data. The first part of our studies are for multiscale Gaussian processes, where we establish Bayesian statistical properties leading to inferences and predictions with probabilistic guarantees. Then we discuss some artificial neural network based models for multiscale data, and compare the results from such models to those using multiscale Gaussian process models. Numerical examples in the context of small area models and neuroimaging data are presented.