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Design and analysis of computer experiments when the output is highly correlated over the input space

Title
Design and analysis of computer experiments when the output is highly correlated over the input space
Authors
Lim Y.B.Sacks J.Studden W.J.Welch W.J.
Ewha Authors
임용빈
SCOPUS Author ID
임용빈scopus
Issue Date
2002
Journal Title
Canadian Journal of Statistics
ISSN
0319-5724JCR Link
Citation
vol. 30, no. 1, pp. 109 - 126
Indexed
SCIE; SCOPUS WOS scopus
Abstract
To build a predictor, the output of a deterministic computer model or "code" is often treated as a realization of a stochastic process indexed by the code's input variables. The authors consider an asymptotic form of the Gaussian correlation function for the stochastic process where the correlation tends to unity. They show that the limiting best linear unbiased predictor involves Lagrange interpolating polynomials; linear model terms are implicitly included. The authors then develop optimal designs based on minimizing the limiting integrated mean squared error of prediction. They show through several examples that these designs lead to good prediction accuracy.
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자연과학대학 > 통계학전공 > Journal papers
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