Prediction for computer experiments with both quantitative and qualitative factors

Min Li, Min-Qian Liu, Xiao-Lei Wang and Yong-Dao Zhou

Statistics & Probability Letters, 2020, vol. 165, issue C

Abstract: Computer experiments with both quantitative and qualitative factors are commonly encountered in practice. Several literatures found that if the cross-correlation between an auxiliary response and the target response (i.e., the response to be predicted) is small, the information of such an auxiliary response may reduce the prediction accuracy of the target response. In this work, we use the prediction accuracy improvement probability to prove the possibility of this case in theory and develop a selection procedure to choose the useful auxiliary responses.

Keywords: Cross-correlation; Multivariate Gaussian process; Prediction accuracy; Sliced Latin hypercube design (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.spl.2020.108858

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