Analysis of computer experiments using penalized likelihood in Gaussian kriging models

Kriging is a popular analysis approach for computer experiments for the purpose of creating a cheap-to-compute "meta-model" as a surrogate to a computationally expensive engineering simulation model. The maximum likelihood approach is used to estimate the parameters in the kriging model. However, the likelihood function near the optimum may be flat in some situations, which leads to maximum likelihood estimates for the parameters in the covariance matrix that have very large variance. To overcome this difficulty, a penalized likelihood approach is proposed for the kriging model. Both theoretical analysis and empirical experience using real world data suggest that the proposed method is particularly important in the context of a computationally intensive simulation model where the number of simulation runs must be kept small because collection of a large sample set is prohibitive. The proposed approach is applied to the reduction of piston slap, an unwanted engine noise due to piston secondary motion. Issues related to practical implementation of the proposed approach are discussed.



Work Title Analysis of computer experiments using penalized likelihood in Gaussian kriging models
Open Access
  1. Runze Li
  2. Agus Sudjianto
  1. Computer experiment
  2. Fisher scoring algorithm
  3. Kriging
  4. Meta-model
  5. Penalized likelihood
  6. Smoothly clipped absolute deviation
License In Copyright (Rights Reserved)
Work Type Article
  1. Technometrics
Publication Date May 2005
Publisher Identifier (DOI)
Deposited July 19, 2022




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Work History

Version 1

  • Created
  • Added Analysis_of_Computer_Experiments_Using_Penalized_Likelihood_in_Gaussian_Kriging_Models.pdf
  • Added Creator Runze Li
  • Added Creator Agus Sudjianto
  • Published
  • Updated Keyword, Publication Date Show Changes
    • Computer experiment, Fisher scoring algorithm, Kriging, Meta-model, Penalized likelihood, Smoothly clipped absolute deviation
    Publication Date
    • 2005-05-01
    • 2005-05
  • Updated