This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 136
Type: Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #306952
Title: Efficient Kriging for Large Spatial Fields
Author(s): Karl Pazdernik*+ and Ranjan Maitra and Douglas Nychka and Stephan Sain
Companies: Iowa State University and Iowa State University and National Center for Atmospheric Research and National Center for Atmospheric Research
Address: 411 WELCH AVE, Ames, IA, 50014,
Keywords: spatial ; Kriging ; gaussian random field ; prediction ; sparse matrix ; efficient
Abstract:

In spatial statistics, a common method for prediction over a Gaussian Random Field (GRF) is Kriging. Unfortunately, Kriging requires inverting a covariance matrix which, depending on the data set, can be extremely large and thus computationally intensive. Thus, I propose a new approach to estimation and prediction that uses a combination of concepts from reduced-rank Kriging, Ridge Regression, and sparse matrix methodology. I will contrast the gains in run time versus the loss in precision, as well as explore the connection between the actual parameter values and the choice of basis functions in the model. This method is applied to a temperature data set of the Midwestern U.S. with very good results.


The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2010 program




2010 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.