On the Relative Importance of Input Factors in Mathematical Models

By Andrea Saltelli, Stefano Tarantola. 2002

444 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Safety, Risk, Reliability and Quality, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dcfa5d01b5bdab9b92e2770392549de80be0a29c

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Citations in the corpus

Random balance designs for the estimation of first order global sensitivity indices (2006, methodology): “This test case has been extensively used by several authors, see [12] for a review.”
Introduction to Sensitivity Analysis (2008, background): “Nonindependence between inputs in SA has been discussed by many authors such as Saltelli and Tarantola [55], Jacques et al.”
Sensitivity analysis practices: Strategies for model-based inference (2006, background): “In [16,52] we discuss settings applicable to different contexts for SA.”
Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators (2005, methodology): “Estimation procedures are offered in Saltelli et al. (2004). The extended variance-based methods, including the improved version of Sobol’, for both dependent and independent input factors, are implemented in the freely distributed software SIMLAB (Saltelli et al.”
Combining Quantitative and Qualitative Measures of Uncertainty in Model‐Based Environmental Assessment: The NUSAP System (2005, methodology): “In a number of projects, we have implemented, demonstrated, and tested a novel approach to uncertainty assessment known as the NUSAP method (Numeral Unit Spread Assessment Pedigree) that complements state-of-the-art quantitative uncertainty methods(5,6)”
Sensitivity in risk analyses with uncertain numbers. (2006, background): “Many researchers in probabilistic sensitivity analysis have suggested that variance is the natural way to measure uncertainty of probability distributions (e.g., Saltelli 2003; Saltelli and Tarantola 2002).”
A Review on Global Sensitivity Analysis Methods (2014, unknown): “The case of functional inputs also receives a growing interest (Iooss and Ribatet [37], Lilburne and Tarantola [55], Saint-Geours et al. [72]), but its treatment in a functional statistical framework remains to be done.”
Probabilistic sensitivity analysis of complex models: a Bayesian approach (2004, background): “The search for the most informative combinations of inputs is considered further by Saltelli and Tarantola (2002).”
Exploring multi-dimensional spaces: a Comparison of Latin Hypercube and Quasi Monte Carlo Sampling Techniques (2015, background): “The important indices in practice are i S and tot i S [31,32].”
SHAPLEY EFFECTS FOR SENSITIVITY ANALYSIS WITH CORRELATED INPUTS: COMPARISONS WITH SOBOL' INDICES, NUMERICAL ESTIMATION AND APPLICATIONS (2017, unknown): “We also provide a discussion of the SA setting [10] that can be addressed with the Shapley effects.”
An effective screening design for sensitivity analysis of large models (2007, background): “ST recognises ‘unimportant’ factors, i.e. factors that can be fixed at any given value over their ranges of uncertainty without affecting significantly the output (Saltelli and Tarantola, 2002).”
Global sensitivity measures from given data (2013, methodology): “Global SA is performed after the uncertainty analysis and allows analysts to obtain additional information about the input-output mapping (Saltelli and Tarantola, 2002; Oakley and O'Hagan, 2004).”
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2010, background): “The case of dependent input factors is not treated in the present paper (see instead [13,31,46,48]).”
A new uncertainty importance measure (2007, methodology): “This problem has then been addressed by Saltelli and Tarantola (2002), which have identi ed two lottery settings for sensitivity analysis in the presence and absence of correlations.”