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The Matrix Revisited: Spatial Interpolation and Smoothing of Large Data Sets
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Doug
Nychka, Geophysical Statistics Project, National Center for Atmospheric
Research
Thursday, 30 October 2003
12:00 – 1:00 PM
W118 Anatomy-Zoology
ABSTRACT
A
spatial analysis often involves manipulating and solving linear systems
based on matrices derived from covariance functions. This talk will present
several
computational
and modeling strategies for dealing with the matrix calculations when spatial data sets are
large. Some of these approaches include iterative
methods
for solving large linear systems and inducing sparsity in the covariance
matrix through tapering . As part of the theory for justifying tapering
there is the tantalizing
connection with kernel methods and some discussion will be given about
equivalences between classical spatial estimators and kernel smoothing.
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