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Activity Number: 17
Type: Topic Contributed
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #315546
Title: Local Asymptotics for Kriging
Author(s): William Kleiber* and Doug Nychka
Companies: University of Colorado and National Center for Atmospheric Research
Keywords: equivalent kernel ; kriging ; mean squared error ; multiresolution ; prediction ; spline smoothing
Abstract:

We develop a theory for examining asymptotic properties of the spatial smoothing problem known as kriging, leading to convenient formulas for the asymptotic mean squared prediction error under kriging. Results are specialized for multiresolution processes, and we present closed form asymptotic bias and variance approximations. We additionally examine the effect of model misspecification, and find that misspecification of the nugget-to-marginal variance ratio (equivalent to the smoothing parameter for splines) leads to a second order penalty on the predictive mean squared error.


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