Probabilistic sensitivity measures as information value

Borgonovo, Emanuele; Hazen, Gordon B.; Jose, Victor Richmond R.; Plischke, Elmar

doi:10.1016/j.ejor.2020.07.010

Cited by 37 publications

(23 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a factor prioritization analysts are interested in determining the inputs that most contribute to the variability of the output. For this setting, we would suggest relying on an ensemble of sensitivity measures, because alternative sensitivity measures consider different aspects of the output response, and one needs to match the quantity of interest with the sensitivity measure (see Borgonovo, Hazen, Jose, & Plischke (2021b) , among others). Moreover, alternative sensitivity measures possess different properties (see Section Appendix A for details).…”

Section: A Goal-oriented Approachmentioning

confidence: 99%

Global sensitivity analysis in epidemiological modeling

Borgonovo

2023

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

Section: A Goal-oriented Approachmentioning

confidence: 99%

Global sensitivity analysis in epidemiological modeling

Borgonovo

2023

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

“…to the unconditional one via some distance. The supremum norm is used in [3] while in [12] the L 2 -norm is chosen. The previous approaches are global.…”

Section: The Cramér-von-mises Indicesmentioning

confidence: 99%

Sensitivity analysis in general metric spaces

Gamboa

Klein

Lagnoux

et al. 2021

Reliability Engineering & System Safety

View full text Add to dashboard Cite

Sensitivity indices are commonly used to quantity the relative inuence of any specic group of input variables on the output of a computer code. In this paper, we introduce new sensitivity indices adapted to outputs valued in general metric spaces. This new class of indices encompasses the classical ones; in particular, the so-called Sobol indices and the Cramér-von-Mises indices. Furthermore, we provide asymptotically Gaussian estimators of these indices based on U-statistics. Surprisingly, we prove the asymptotic normality straightforwardly. Finally, we illustrate this new procedure on a toy model and on two real-data examples.

show abstract

“…Remark 2. Probabilistic sensitivity measures are typically used for importance measurement and take values in [0, 1]; with 1 being the most important input factor and 0 being (desirably) independent from the output (Borgonovo et al 2021). This is in contrast to our framework, where…”

Section: Sensitivity Measuresmentioning

confidence: 99%

Reverse Sensitivity Analysis for Risk Modelling

Pesenti¹

2021

Preprint

View full text Add to dashboard Cite

We consider the problem where a modeller conducts sensitivity analysis of a model consisting of random input factors, a corresponding random output of interest, and a baseline probability measure. The modeller seeks to understand how the model (the distribution of the input factors as well as the output) changes under a stress on the output's distribution. Specifically, for a stress on the output random variable, we derive the unique stressed distribution of the output that is closest in the Wasserstein distance to the baseline output's distribution and satisfies the stress. We further derive the stressed model, including the stressed distribution of the inputs, which can be calculated in a numerically efficient way from a set of baseline Monte Carlo samples.The proposed reverse sensitivity analysis framework is model-free and allows for stresses on the output such as (a) the mean and variance, (b) any distortion risk measure including the Value-at-Risk and Expected-Shortfall, and (c) expected utility type constraints, thus making the reverse sensitivity analysis framework suitable for risk models.

show abstract

Probabilistic sensitivity measures as information value

Cited by 37 publications

References 55 publications

Global sensitivity analysis in epidemiological modeling

Global sensitivity analysis in epidemiological modeling

Sensitivity analysis in general metric spaces

Reverse Sensitivity Analysis for Risk Modelling

Contact Info

Product

Resources

About