The Matrix Revisited:  Spatial Interpolation and Smoothing of Large Data Sets

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.

 

 

 

 


 

 

 

College of Natural Sciences


 

 

 

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