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QICID: 30508
Title: Prediction for Computer Experiments Having Quantitative and Qualitative Input Variables
Copyright: ASQ; American Statistical Association
Author: Han, Gang; Santner, Thomas J.; Notz, William I.; Bartel, Donald L.
Organization: H. Lee Moffitt Cancer Center and Research Institute; Department of Statistics, The Ohio State University; Sibley School of Mechanical and Aerospace Engineering
Subject: Stochastic models; Gaussian curve; Bayesian methods; Hierarchical experiments; Prediction; Mean square errors (MSE);
Series: Technometrics, Vol. 51, No. 3, August 2009, pp. 278–288
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Abstract: [This abstract is based on the authors' abstract.]
A hierarchical Bayesian model with conditional Gaussian stochastic process components is proposed for the prediction of the output from a computer code having both quantitative and qualitative inputs. The model assumes that the outputs corresponding to different levels of a qualitative input have similar functional behavior in the quantitative inputs. The method is compared to alternative proposals and is illustrated in a biomechanical engineering example.
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