Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models

By C. Storlie, Laura Painton Swiler, Jon C. Helton, Cédric J. Sallaberry. Reliability Engineering & System Safety, 2009

367 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/37815fa68db42b63c71ec4d189ee14c4385e7a71

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In Computational Theory and Mathematics

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Citations in the corpus

Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015, methodology): “…al., 2003), response surface approximation (Helton and Davis, 2003), Fourier series (Saltelli et al., 1999) nonparametric regression (Helton, 1993; Storlie et al., 2009), Kriging (Kleijnen, 2009; Borgonovo et al., 2012; Lamoureux et al., 2014), Gauss process (Rasmussen and Williams, 2006),…”
Asymptotic normality and efficiency of two Sobol index estimators (2013, background): “imit where the sample size goes to inﬁnity and the metamodel converges to the true model. Some work has been done on the non-asymptotic error quantiﬁcation in Sobol index estimation in earlier papers [13, 16, 29] by means of conﬁdence intervals which account for both sampling and metamodel errors. I”
Metamodel-based sensitivity analysis: polynomial chaos expansions and Gaussian processes (2016, neutral): “For a general introduction to surrogate modelling the reader is referred to [55] and to the recent review by Iooss and Lemâıtre [29].”
Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, positive): “The immediate advantage is that it can simplify computationally intensive models and thus enables much faster model runs (Storlie et al., 2009), especially for a complex hydrological model with high computational cost of hundreds or thousands of model runs.”
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2010, methodology): “Methods based on meta-modeling and emulators have also been tried to estimate the total sensitivity indices [44,47].”
Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning (2011, background): “Notable exceptions are presented by Storlie et al. (2009) and Taddy et al. (2009).”
A Review on Global Sensitivity Analysis Methods (2014, neutral): “[88] propose a bootstrap method for estimating the impact of the metamodel error.”
Sensitivity Analysis for Hydraulic Models (2009, positive): “An alternative to the direct use of sampling-based sensitivity methods is to construct a computationally efﬁcient emulator of the numerical model in question and conduct sensitivity analysis using the emulator function (cid:1) Storlie et al. 2008 (cid:2) .”
An Intercomparison of Sampling Methods for Uncertainty Quantification of Environmental Dynamic Models (2015, methodology): “The SA method used for this purpose is the RSMSobol’ method, a surrogate modeling based quantitative sensitivity analysis method that calculates both the main and total effect of each parameter (Sobol', 1993; Sobol', 2001; Storlie et al., 2009).”
A Generalized ANOVA Dimensional Decomposition for Dependent Probability Measures (2014, neutral): “There exist a multitudes of methods or techniques for calculating the global sensitivity indices of a function of independent variables: the random balance design method [31], state-dependent parameter metamodel [22], Sobol’s method [26], polynomial chaos expansion [30], polynomial dimensional decom”
Using recursive algorithms for the efficient identification of smoothing spline ANOVA models (2010, background): “Storlie et al. (2008) performed an extensive analysis and comparison of metamodeling approaches for the estimation of total sensitivity indices.”
Variance-based sensitivity analysis of model outputs using surrogate models (2011, background): “Storlie and Helton (2007) and Storlie et al. (2009) examined how a variety of regression smoothers can facilitate VBSA.”
Polynomial-Chaos-based Kriging (2015, methodology): “To circumvent this problem, surrogate models may be used, which replace the original computational model by an easy-to-evaluate function (Storlie et al., 2009; Hastie et al., 2001; Forrester et al., 2008).”