JSM 2011 Online Program

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Abstract Details

Activity Number: 322
Type: Invited
Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #300314
Title: Computationally Feasible Hierarchical Modeling Strategies for Large Spatial Data Sets
Author(s): Rajarshi Guhaniyogi*+ and Sudipto Banerjee
Companies: University of Minnesota and University of Minnesota
Address: A460 MAYO BUILDING, MAIL CODE 303, MINNEAPOLIS, MN, MN55455,
Keywords: Bayesian modeling ; Low-rank Gaussian processes ; Hierarchical modeling ; Markov chain Monte Carlo ; Spatial data ; Spatial super-populations
Abstract:

Large point referenced datasets are common in the environmental and natural sciences. The computational burden in fitting large spatial datasets undermines estimation of Bayesian models. We explore several improvements low-rank and other scalable spatial process models including reduction of biases and process-based modeling of ``centers'' or ``knots'' that determine optimal subspaces for data projection. We also consider alternate strategies for handling massive spatial datasets. One approach concerns developing process-based super-population models and developing Bayesian finite-population sampling techniques for spatial data. We also explore model-based simultaneous dimension-reduction in space, time and the number of variables. Flexible and rich hierarchical modeling applications in forestry are demonstrated.


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