Quantile sensitivity measures based on subset simulation importance sampling

Abstract Global sensitivity measures based on quantiles of the output are an efficient tool in measuring the effect of input variables for problems in which α − th quantiles are the functions of interest and for identification of inputs which are the most important in achieving the specific values of the model output. Previously proposed methods for numerical estimation of such measures are costly and not practically feasible in cases in which the quantile level α is very small or high. It is shown that the subset simulation importance sampling (SS-IS) method previously applied for solving small failure probability problems can be efficiently used for estimating quantile global sensitivity measures (QGSM). Considered test cases and engineering examples show that the proposed SS-IS method is more efficient than the previously proposed Monte Carlo method.