Improved generalized Fourier amplitude sensitivity test (FAST) for model assessment

Shoufan Fang, George Z. Gertner, Svetlana Shinkareva, Guangxing Wang, Alan Anderson

Research output: Contribution to journalArticlepeer-review


The Fourier amplitude sensitivity test (FAST) can be used to calculate the relative variance contribution of model input parameters to the variance of predictions made with functional models. It is widely used in the analyses of complicated process modeling systems. This study provides an improved transformation procedure of the Fourier amplitude sensitivity test (FAST) for non-uniform distributions that can be used to represent the input parameters. Here it is proposed that the cumulative probability be used instead of probability density when transforming non-uniform distributions for FAST. This improvement will increase the accuracy of transformation by reducing errors, and makes the transformation more convenient to be used in practice. In an evaluation of the procedure, the improved procedure was demonstrated to have very high accuracy in comparison to the procedure that is currently widely in use.

Original languageEnglish (US)
Pages (from-to)221-226
Number of pages6
JournalStatistics and Computing
Issue number3
StatePublished - Aug 2003


  • Fourier amplitude sensitivity test (FAST)
  • Model assessment
  • Sensitivity analysis
  • Uncertainty assessment

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics


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