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Local Polynomial Estimation for Sensitivity Analysis on Models With Correlated Inputs

Sébastien da Veiga · François Wahl · Fabrice Gamboa

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Probabilistic and Robust Engineering Design Advanced Statistical Methods and Models Gaussian Processes and Bayesian Inference

Abstract

Sensitivity indexes when the inputs of a model are not independent are derived from local polynomial techniques. Two original estimators based on local polynomial smoothers are proposed. Both have good theoretical properties, which are illustrated through analytical examples. Comparison with the Bayesian approach developed by Oakley and O’Hagan (2004) is also performed. The two proposed estimators are used to carry out a sensitivity analysis on two real case models with correlated parameters.

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1989Design and Analysis of Computer Experiments 2004Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach 1973Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory 2002Sensitivity Analysis in Practice 1998Efficient estimation of conditional variance functions in stochastic regression 2006Sensitivity analysis in presence of model uncertainty and correlated inputs

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2017Weights and importance in composite indicators: Closing the gap 2007Introduction to Sensitivity Analysis 2017Introduction to Sensitivity Analysis 2015Non-parametric methods for global sensitivity analysis of model output with dependent inputs 2017Global sensitivity analysis using support vector regression 2010Computer experiments: a review 2016Global sensitivity analysis using low-rank tensor approximations 2008Global sensitivity analysis of computer models with functional inputs

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