Statistical Validation of Engineering and Scientific Models: A Maximum Likelihood Based Metric

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Two major issues associated with model validation are addressed here. First, we present a maximum likelihood approach to define and evaluate a model validation metric. The advantage of this approach is it is more easily applied to nonlinear problems than the methods presented earlier by Hills and Trucano (1999, 2001); the method is based on optimization for which software packages are readily available; and the method can more easily be extended to handle measurement uncertainty and prediction uncertainty with different probability structures. Several examples are presented utilizing this metric. We show conditions under which this approach reduces to the approach ... continued below

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86 pages

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HILLS, RICHARD GUY & TRUCANO, TIMOTHY G. January 1, 2002.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM, and Livermore, CA (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

Two major issues associated with model validation are addressed here. First, we present a maximum likelihood approach to define and evaluate a model validation metric. The advantage of this approach is it is more easily applied to nonlinear problems than the methods presented earlier by Hills and Trucano (1999, 2001); the method is based on optimization for which software packages are readily available; and the method can more easily be extended to handle measurement uncertainty and prediction uncertainty with different probability structures. Several examples are presented utilizing this metric. We show conditions under which this approach reduces to the approach developed previously by Hills and Trucano (2001). Secondly, we expand our earlier discussions (Hills and Trucano, 1999, 2001) on the impact of multivariate correlation and the effect of this on model validation metrics. We show that ignoring correlation in multivariate data can lead to misleading results, such as rejecting a good model when sufficient evidence to do so is not available.

Physical Description

86 pages

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  • Other Information: PBD: 1 Jan 2002

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  • Report No.: SAND2001-1783
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/791881 | External Link
  • Office of Scientific & Technical Information Report Number: 791881
  • Archival Resource Key: ark:/67531/metadc735861

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  • January 1, 2002

Added to The UNT Digital Library

  • Oct. 19, 2015, 7:39 p.m.

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  • April 12, 2016, 3 p.m.

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HILLS, RICHARD GUY & TRUCANO, TIMOTHY G. Statistical Validation of Engineering and Scientific Models: A Maximum Likelihood Based Metric, report, January 1, 2002; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc735861/: accessed October 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.