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Screening pp 308-327 | Cite as

Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization

  • Matthias Schonlau
  • William J. Welch

Abstract

A nexperiment involving a complex computer model or code may have tens or even hundreds of input variables and, hence, the identification of the more important variables (screening) is often crucial. Methods are described for decomposing a complex input—output relationship into effects. Effects are more easily understood because each is due to only one or a small number of input variables. They can be assessed for importance either visually or via a functional analysis of variance. Effects are estimated from flexible approximations to the input—output relationships of the computer model. This allows complex nonlinear and interaction relationships to be identified. The methodology is demonstrated on a computer model of the relationship between environmental policy and the world economy.

Keywords

Marginal Effect Computer Experiment Human Development Index Corrected Effect Output Relationship 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Matthias Schonlau
    • 1
  • William J. Welch
    • 2
  1. 1.RAND CorporationPittsburghUSA
  2. 2.Department of StatisticsUniversity of British ColumbiaVancouverCanada

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