Statistics and Computing

, Volume 22, Issue 3, pp 713–722 | Cite as

Cases for the nugget in modeling computer experiments

Article

Abstract

Most surrogate models for computer experiments are interpolators, and the most common interpolator is a Gaussian process (GP) that deliberately omits a small-scale (measurement) error term called the nugget. The explanation is that computer experiments are, by definition, “deterministic”, and so there is no measurement error. We think this is too narrow a focus for a computer experiment and a statistically inefficient way to model them. We show that estimating a (non-zero) nugget can lead to surrogate models with better statistical properties, such as predictive accuracy and coverage, in a variety of common situations.

Keywords

Computer simulator Surrogate model Gaussian process Interpolation Smoothing 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Booth School of BusinessUniversity of ChicagoChicagoUSA
  2. 2.Applied Math & StatisticsUniversity of California, Santa CruzSanta CruzUSA

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