A Framework for Model Validation

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Computational models have the potential of being used to make credible predictions in place of physical testing in many contexts, but success and acceptance require a convincing model validation. In general, model validation is understood to be a comparison of model predictions to experimental results but there appears to be no standard framework for conducting this comparison. This paper gives a statistical framework for the problem of model validation that is quite analogous to calibration, with the basic goal being to design and analyze a set of experiments to obtain information pertaining to the `limits of error' that can be ... continued below

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Easterling, R.G. February 2, 1999.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Laboratories, Albuquerque, NM, and Livermore, CA
    Place of Publication: Albuquerque, New Mexico

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Description

Computational models have the potential of being used to make credible predictions in place of physical testing in many contexts, but success and acceptance require a convincing model validation. In general, model validation is understood to be a comparison of model predictions to experimental results but there appears to be no standard framework for conducting this comparison. This paper gives a statistical framework for the problem of model validation that is quite analogous to calibration, with the basic goal being to design and analyze a set of experiments to obtain information pertaining to the `limits of error' that can be associated with model predictions. Implementation, though, in the context of complex, high-dimensioned models, poses a considerable challenge for the development of appropriate statistical methods and for the interaction of statisticians with model developers and experimentalists. The proposed framework provides a vehicle for communication between modelers, experimentalists, and the analysts and decision-makers who must rely on model predictions.

Source

  • 1998 U.S. Army Conference on Applied Statistics; Las Cruces, NM; 10/21-23/1998

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  • Other: DE00003351
  • Report No.: SAND99-0301C
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 3351
  • Archival Resource Key: ark:/67531/metadc682745

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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Creation Date

  • February 2, 1999

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

Description Last Updated

  • Nov. 28, 2016, 6:02 p.m.

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Easterling, R.G. A Framework for Model Validation, article, February 2, 1999; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc682745/: accessed June 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.