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Abstract Details
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Activity Number:
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390
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Type:
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Invited
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Technometrics
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| Abstract - #300267 |
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Title:
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Regression-Based Inverse Distance Weighting with Applications to Computer Experiments
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Author(s):
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Lulu Kang*+ and Roshan Joseph Vengazhiyil
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Companies:
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Illinois Institute of Technology and Georgia Institute of Technology
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Address:
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Department of Applied Mathematics, Chicago, IL, 60616,
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Keywords:
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confidence interval ;
Kriging ;
multivariate interpolation
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Abstract:
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Inverse distance weighting (IDW) is a simple method for multivariate interpolation but has poor prediction accuracy. In this article we show that the prediction accu- racy of IDW can be substantially improved by integrating it with a linear regression model. This new predictor is quite flexible, computationally efficient, and works well in problems having high dimensions and/or large data sets. We also develop a heuristic method for constructing confidence intervals for prediction.
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