JSM 2005 - Toronto

Abstract #302761

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 307
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: Technometrics
Abstract - #302761
Title: Computer Experiments Using Penalized Likelihood in Gaussian Kriging Models
Author(s): Agus Sudjianto*+ and Runze Li
Companies: Bank of America and The Pennsylvania State University
Address: Risk Management Quality & Productivity, Charlotte, NC, 28255-0001,
Keywords:
Abstract:

Kriging is a popular analysis approach for computer experiments for the purpose of creating a cheap-to-compute "metamodel" as a surrogate to a computationally expensive engineering simulation model. The maximum likelihood approach is employed to estimate the parameters in the kriging model. However, this can lead 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 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. We apply the proposed approach 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.

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Revised March 2005