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Identifying Important Observations Using Cross Validation and Computationally Frugal Sensitivity Analysis Methods

Dates

Year
2010

Citation

Foglia, Laura, Hill, Mary C, Mehl, Steffen W, Perona, Paolo, and Burlando, Paolo, 2010, Identifying Important Observations Using Cross Validation and Computationally Frugal Sensitivity Analysis Methods: Procedia - Social and Behavioral Sciences, v. 2, iss. 6, p. 7650-7651.

Summary

Sensitivity analysis methods are used to identify measurements most likely to provide important information for model development and predictions. Methods range from computationally demanding Monte Carlo and cross-validation methods that require thousands to millions of model runs, to very computationally efficient linear methods able to account for interrelations between parameters that involve tens to hundreds of runs. Some argue that because linear methods neglect the effects of model nonlinearity, they are not worth considering. However, when faced with computationally demanding models needed to simulate, for example, climate change, the chance of obtaining insights with so few model runs is tempting. This work compares results [...]

Contacts

Attached Files

Communities

  • USGS National Research Program

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Provenance

Added to ScienceBase on Fri Apr 19 10:54:09 MDT 2013 by processing file <b>Modeling and uncertainty of complex ground-water systems.xml</b> in item <a href="https://www.sciencebase.gov/catalog/item/504216b8e4b04b508bfd3357">https://www.sciencebase.gov/catalog/item/504216b8e4b04b508bfd3357</a>

Additional Information

Identifiers

Type Scheme Key
DOI http://sciencebase.gov/vocab/identifierScheme 10.1016/j.sbspro.2010.05.161

Citation Extension

citationTypeJournal Article
journalProcedia - Social and Behavioral Sciences
parts
typePages
value7650-7651
typeVolume
value2
typeIssue
value6

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