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Abstract Details

Activity Number: 632
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #309078
Title: Bootstrap-Based Trans-Gaussian Kriging
Author(s): Krista Rister*+ and Soumendra Nath Lahiri
Companies: Texas A&M University and Texas A&M University
Address: , , ,
Keywords: boostrap ; Kriging ; Trans-Gaussian
Abstract:

In spatial statistics, the reliability of Kriging prediction methods relies on the observations coming from an underlying Gaussian process. When the observed data set is not from a multivariate Gaussian distribution, but rather is a transformation of Gaussian data, Kriging methods can produce biased predictions. Bootstrap resampling methods present a potential bias correction. We propose a parametric bootstrap methodology for the calculation of a multiplicative bias correction factor when dealing with trans-Gaussian data. Furthermore, we investigate the asymptotic properties. Finally, we present the results for both simulated and real world data.


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