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Screening, Predicting, and Computer Experiments

 

作者: WilliamJ. Welch,   Robert.J. Buck,   Jerome Sacks,   HenryP. Wynn,   TobyJ. Mitchell,   MaxD. Morris,  

 

期刊: Technometrics  (Taylor Available online 1992)
卷期: Volume 34, issue 1  

页码: 15-25

 

ISSN:0040-1706

 

年代: 1992

 

DOI:10.1080/00401706.1992.10485229

 

出版商: Taylor & Francis Group

 

关键词: Computer code;Data-adaptive modeling;Dimension reduction;Electroniccircuit simulation;Maximum likelihood;Simulation;Stochastic process

 

数据来源: Taylor

 

摘要:

Many scientific phenomena are now investigated by complex computer models or codes. Given the input values, the code produces one or more outputs via a complex mathematical model. Often the code is expensive to run, and it may be necessary to build a computationally cheaper predictor to enable, for example, optimization of the inputs. If there are many input factors, an initial step in building a predictor is identifying (screening) the active factors. We model the output of the computer code as the realization of a stochastic process. This model has a number of advantages. First, it provides a statistical basis, via the likelihood, for a stepwise algorithm to determine the important factors. Second, it is very flexible, allowing nonlinear and interaction effects to emerge without explicitly modeling such effects. Third, the same data are used for screening and building the predictor, so expensive runs are efficiently used. We illustrate the methodology with two examples, both having 20 input variables. In these examples, we identify the important variables, detect curvature and interactions, and produce a useful predictor with 30–50 runs of the computer code.

 

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