Global Sensitivity Analysis with Mixtures: A Generalized Functional ANOVA Approach

Functional ANOVA Models for Generalized Regression

Functional ANOVA models are considered in the context of generalized regression, which includes logistic regression, probit regression and Poisson regression as special cases. The multivariate predictor function is modeled as a speci ed sum of a constant term, main e ects and interaction terms. Maximum likelihood estimates are used, where the maximizations are taken over suitably chosen approxi...

Functional ANOVA Models: Convex-concave approach and concurvity analysis

This paper aims at bridging a gap between functional ANOVA modeling and recent advances in machine learning and kernel-based models. Functional ANOVA on the one hand extends linear ANOVA techniques to nonlinear multivariate models as smoothing splines, aiming to provide interpretability to an estimate and handling the curse of dimensionality. Multiple kernel learning (MKL) on the other hand is ...

Uncertainty quantification and global sensitivity analysis of complex chemical process using a generalized polynomial chaos approach

Uncertainties are ubiquitous and unavoidable in process design and modeling. Because they can significantly affect the safety, reliability and economic decisions, it is important to quantify these uncertainties and reflect their propagation effect to process design. This paper proposes the application of generalized polynomial chaos (gPC)-based approach for uncertainty quantification and sensit...

Global sensitivity analysis of computer models with functional inputs

Global sensitivity analysis is used to quantify the influence of uncertain input parameters on the response variability of a numerical model. The common quantitative methods are appropriate with computer codes having scalar input variables. This paper aims at illustrating different variance-based sensitivity analysis techniques, based on the so-called Sobol’s indices, when some input variables ...

Design of Experiment in Global Sensitivity Analysis Based on ANOVA High-Dimensional Model Representation