Design and analysis of computer experiments when the output is highly correlated over the input space

DOI10.2307/3315868zbMath1003.62082OpenAlexW2118097554MaRDI QIDQ3148216

Jerome Sacks, Yong B. Lim, William J. Welch, William J. Studden

Publication date: 26 November 2002

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3315868

zbMATH Keywords

interpolationbest linear unbiased predictionintegrated mean squared errorcomputer code

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Nonparametric regression and quantile regression (62G08) Gaussian processes (60G15) Optimal statistical designs (62K05) Inference from stochastic processes (62M99) Prediction theory (aspects of stochastic processes) (60G25)

Related Items

Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process ⋮ Gaussian process classification: Singly versus doubly stochastic models, and new computational schemes ⋮ Uniform Design for Experiments with Mixtures ⋮ Regression with strongly correlated data ⋮ Analysis methods for computer experiments: how to assess and what counts? ⋮ Optimal designs for the identification of the order of a Fourier regression ⋮ Spatial sampling design and covariance-robust minimax prediction based on convex design ideas ⋮ Maximum Likelihood Estimation for a Smooth Gaussian Random Field Model

Cites Work

• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
• Circuit Optimization via Sequential Computer Experiments: Design of an Output Buffer