- stepwise rank regression analysis for building input-output models to identify key contributors to output variance,
- mutual information (entropy) analysis for determining the strength of nonmonotonic patterns of input-output association, and
- classification tree analysis for determining what variables or combinations are responsible for driving model output into extreme categories.
INTERA’s Srikanta Mishra Publishes Review Paper on Global Sensitivity Analysis Techniques
Dr. Srikanta Mishra, INTERA's Director of Waste Isolation Services, recently published an article in Ground Water entitled, "Global Sensitivity Analysis Techniques for Probabilistic Ground Water Modeling." The article, co-authored by INTERA staff members Neil Deeds and Greg Ruskauff, describes global sensitivity analysis techniques that are better suited for analyzing input-output relationships over the full range of parameter variations and model outcomes, as opposed to local sensitivity analyses carried out around a reference point. The article describes three techniques that are best applied in conjunction with Monte Carlo simulation-based probabilistic analyses: