Prof. Michael Stein : A Modeling Approach for Large Spatial Datasets

Posted by , part of the Statistics and Data Science Seminar.

At

Sept. 5, 2007, 3:30 p.m.

In

SEO 636

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.