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