Global sensitivity measures from given data

By Elmar Plischke, Emanuele Borgonovo, Curtis L. Smith. European Journal of Operational Research, 2013

398 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

SALib: An open-source Python library for Sensitivity Analysis (2017, background): “…and Saltelli (2007)), FAST (Cukier et al. 1973, A. Saltelli, Tarantola, and Chan (1999)), Delta Moment-Independent Measure (E. Borgonovo 2007, Plischke, Borgonovo, and Smith (2013)) Derivative-based Global Sensitivity Measure (DGSM) (Sobol’ and Kucherenko 2009) , and Fractional Factorial…”
A simple and efficient method for global sensitivity analysis based on cumulative distribution functions (2015, positive): “, 2006) and the d-sensitivity measure (Borgonovo, 2007; Plischke et al., 2013) follow this line of reasoning.”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, neutral): “Moreover, the recent emergence of ‘given-data ’ SA methods will provide unprecedented opportunities for model diagnostic testing, as they can directly be applied to observed data as well, in addition to the mathematical models (Plischke et al., 2013; Sheikholeslami and Razavi, 2020).”
New improvements in the use of dependence measures for sensitivity analysis and screening (2014, background): “Table 2 and Figure 1 show that the same phenomenon occurs with the Borgonovo’s delta moment independent measure [39] defined by δk = (1/2) ∫ Xk fXk(x) ∫ Y |fY(y)− fY|Xk=x(y)| dy dx, where fXk ,”