New improvements in the use of dependence measures for sensitivity analysis and screening
By Matthias De Lozzo, Amandine Marrel. 2014
59 citations recorded by the source corpus.
ABSTRACT Physical phenomena are commonly modelled by time consuming numerical simulators, function of many uncertain parameters whose influences can be measured via a global sensitivity analysis. The usual variance-based indices require too many simulations, especially as the inputs are numerous. To address this limitation, we consider recent advances in dependence measures, focusing on the distance correlation and the Hilbert–Schmidt independence criterion. We study and use these indices for a screening purpose. Numerical tests reveal differences between variance-based indices and dependence measures. Then, two approaches are proposed to use the latter for a screening purpose. The first approach uses independence tests, with existing asymptotic versions and spectral extensions; bootstrap versions are also proposed. The second considers a linear model with dependence measures, coupled to a bootstrap selection method or a Lasso penalization. Numerical experiments show their potential in the presence of many non-influential inputs and give successful results for a nuclear reliability application.
Subfields: Aerospace Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/c3ce4a6e2e67722e1e95b09194758226a77b5793
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
In Statistics and Probability
Citations in the corpus
- Causal Discovery with Reinforcement Learning (2019, methodology): “To make it robust to data scales, we use the normalized version of HSIC [7].”
- The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “…and kernel-based indices used in ML (Gretton et al., 2005) have been introduced to the SA community by Da Veiga (2015) and further extended by De Lozzo and Marrel (2016), especially in regard to the Hilbert-Schmidt Independence Criterion (HSIC) which detects features that are non-influential…”