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Abstract Details
Activity Number:
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16
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract - #300971 |
Title:
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Filtered Kriging for Spatial Data with Heterogeneous Measurement Error Variances
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Author(s):
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William F. Christensen*+
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Companies:
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Brigham Young University
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Address:
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Department of Statistics, Provo, UT, 84602,
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Keywords:
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heterogeneous variance measurement error filtered kriging ;
HFK ;
spatial smoothing ;
spatial prediction ;
NARCCAP
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Abstract:
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When predicting values for the measurement-error-free component of an observed spatial process, it is generally assumed that the process has a common measurement error variance. However, it is often the case that each measurement in a spatial data set has a known, site-specific measurement error variance, rendering the observed process nonstationary. We present a simple approach for estimating the semivariogram of the unobservable measurement-error-free process using a bias-adjustment of the classical semivariogram formula. We then develop a new kriging predictor which filters the measurement errors. For scenarios where each site's measurement error variance is a function of the process of interest, we recommend an approach which also uses a variance-stabilizing transformation. The properties of the heterogeneous variance measurement-error-filtered kriging (HFK) predictor and variance-stabilized HFK predictor, and the improvement of these approaches over standard measurement-error filtered kriging are demonstrated using simulation. The approach is illustrated with climate model output from the Hudson Strait area in northern Canada.
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