Abstract
For deterministic computer simulations, Gaussian process models are a standard procedure for fitting data. These models can be used only when the study design avoids having replicated points. This characteristic is also desirable for one-dimensional projections of the design, since it may happen that one of the design factors has a strongly nonlinear effect on the response. Latin hypercube designs have uniform one-dimensional projections, but are not efficient for fitting low-order polynomials when there is a small error variance. D-optimal designs are very efficient for polynomial fitting but have substantial replication in projections. We propose a new class of designs that bridge the gap between D-optimal designs and D-optimal Latin hypercube designs. These designs guarantee a minimum distance between points in any one-dimensional projection allowing for the fit of either polynomial or Gaussian process models. Subject to this constraint they are D-optimal for a prespecified model.
Original language | English (US) |
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Pages (from-to) | 155-163 |
Number of pages | 9 |
Journal | Technometrics |
Volume | 57 |
Issue number | 2 |
DOIs | |
State | Published - Apr 3 2015 |
Keywords
- Computer experiments
- Gaussian process model
- Optimal design
- Space-filling designs
ASJC Scopus subject areas
- Statistics and Probability
- Modeling and Simulation
- Applied Mathematics
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Bridge Designs for Modeling Systems With Low Noise
Jones, B. (Creator), Silvestrini, R. T. (Creator), Montgomery, D. (Creator) & Steinberg, D. M. (Creator), figshare Academic Research System, 2015
DOI: 10.6084/m9.figshare.1481268.v1, https://tandf.figshare.com/articles/dataset/Bridge_Designs_for_Modeling_Systems_With_Low_Noise/1481268/1
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