Statistics and Data Science Seminar
Prof. Michael Stein
University of Chicago
A Modeling Approach for Large Spatial Datasets
Abstract: For Gaussian spatial processes observed at a large number of irregularly sited
locations, exact calculation of the likelihood is generally not possible due to
both memory and computational constraints. If we can write the covariance matrix
of the observations as a sparse matrix plus a matrix of moderate rank, then both
the number of computations and memory requirements can be greatly reduced. The
idea is that the sparse term will capture the local behavior of the process and
the low rank term the large-scale behavior. This approach is applied to compute
likelihood-based estimates of the spatial covariance structure for total column
ozone measurements on a global scale. The approach can be judged a success
computationally in that likelihoods can be calculated exactly for datasets far
too large to carry out the computations for a more general model. However,
various diagnostics show problems with the model, so that further work is needed.
Tea at 3:15pm.
Wednesday September 5, 2007 at 3:30 PM in SEO 636