Measuring variable importance for computational models or measured data is an important
task in many applications. It has drawn our attention that the variable importance analysis …

Sensitivity analysis (SA) aims to identify the key parameters that affect model performance
and it plays important roles in model parameterization, calibration, optimization, and …

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling.
The tremendous potential benefits of SA are, however, yet to be fully realized, both for …

Composite indicators are very popular tools for assessing and ranking countries and
institutions in terms of environmental performance, sustainability, and other complex …

We provide a sensitivity analysis toolbox consisting of a set of Matlab functions that offer
utilities for quantifying the influence of uncertain input parameters on uncertain model …

In many fields, such as environmental risk assessment, agronomic system behavior,
aerospace engineering, and nuclear safety, mathematical models turned into computer code …

Plant functional traits determine vegetation responses to environmental variation, but
variation in trait values is large, even within a single site. Likewise, uncertainty in how these …

The model-based system engineering approach consists of assembling subsystems
together to model a complete system. In this context, some functional blocks can have a …

A novel approach for estimation variance-based sensitivity indices for models with
dependent variables is presented. Both the first order and total sensitivity indices are derived …

Global sensitivity analysis aims at quantifying the relative importance of uncertain input
variables onto the response of a mathematical model of a physical system. ANOVA-based …