Modeling Data from Computer Experiments: An Empirical Comparison of Kriging with MARS and Projection Pursuit Regression

Einat Neumann Ben-Ari · David M. Steinberg

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Optimal Experimental Design Methods Statistical Methods and Applications Gaussian Processes and Bayesian Inference

Abstract

Computer experiments enable scientists to study complex processes by running computer codes that simulate them. We consider here the analysis of data from computer experiments, comparing three methods for non-parametric smoothing of high-dimensional data: Kriging, Multivariate Adaptive Regression Splines (MARS) and Projection Pursuit Regression (PPR). On several data sets of varying complexity, we find that Kriging is consistently the best performer.

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2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 1989Design and Analysis of Computer Experiments 1979A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 2003The Design and Analysis of Computer Experiments 2004Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach 2000Uniform Design: Theory and Application 1992Screening, Predicting, and Computer Experiments 2004Combining Field Data and Computer Simulations for Calibration and Prediction

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