Quantile based global sensitivity measures
This work provides a novel method for global sensitivity analysis focused on quantiles, benefiting practitioners in structural safety and related fields where extreme values are critical.
The paper introduces new global sensitivity measures based on output quantiles, useful for analyzing problems where quantiles are of interest or for identifying variables important for extreme outputs. Numerical results demonstrate the efficiency of the proposed method, with the double loop reordering estimator being much more efficient than the brute force estimator.
New global sensitivity measures based on quantiles of the output are introduced. Such measures can be used for global sensitivity analysis of problems in which quantiles are explicitly the functions of interest and for identification of variables which are the most important in achieving extreme values of the model output. It is proven that there is a link between introduced measures and Sobol main effect sensitivity indices. Two different Monte Carlo estimators are considered. It is shown that the double loop reordering approach is much more efficient than the brute force estimator. Several test cases and practical case studies related to structural safety are used to illustrate the developed method. Results of numerical calculations show the efficiency of the presented technique.