Title: A new sample-based algorithms to compute the total sensitivity index
Publisher: Cornell University Library
Publication Year: 2016
JRC N°: JRC105263
URI: http://arxiv.org/abs/1703.05799
Type: Articles in periodicals and books
Abstract: Variance based sensitivity indices have established themselves as a reference among practitioners of sensitivity analysis of model output. It is not unusual to consider a variance based sensitivity analysis as informative if it produces at least the first order sensitivity indices S_j and the so-called total-effect sensitivity indices S_Tj or T_j for all the uncertain factors of the mathematical model under analysis. Computational economy is critical in sensitivity analysis. It depends mostly upon the number of model evaluations needed to obtain stable values of the estimates. While for the first order indices efficient estimation procedures are available which are independent from the number of factors under analysis, this is less the case for the total sensitivity indices. When estimating the T_j one can either use a sample based approach, whose computational cost depends from the number of factors, or approaches based on meta-modelling / emulators, e.g. based on Gaussian processes. The present work focuses on sample-based estimation procedures for T_j, and tries different avenues to achieve an algorithmic improvement over the designs proposed in the existing best practices. One among the selected procedures appear to lead to a considerable improvement when the mean absolute error is considered.
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