Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models
This work addresses the problem of quantifying input impact in stochastic computational models for fields like finance and epidemiology, representing an incremental advancement over existing methods.
The paper tackles global sensitivity analysis for stochastic simulators by proposing a method based on generalized lambda surrogate models, which estimates sensitivity indices without requiring replications and demonstrates convergence in examples with heteroskedasticity and low signal-to-noise ratios.
Global sensitivity analysis aims at quantifying the impact of input variability onto the variation of the response of a computational model. It has been widely applied to deterministic simulators, for which a set of input parameters has a unique corresponding output value. Stochastic simulators, however, have intrinsic randomness due to their use of (pseudo)random numbers, so they give different results when run twice with the same input parameters but non-common random numbers. Due to this random nature, conventional Sobol' indices, used in global sensitivity analysis, can be extended to stochastic simulators in different ways. In this paper, we discuss three possible extensions and focus on those that depend only on the statistical dependence between input and output. This choice ignores the detailed data generating process involving the internal randomness, and can thus be applied to a wider class of problems. We propose to use the generalized lambda model to emulate the response distribution of stochastic simulators. Such a surrogate can be constructed without the need for replications. The proposed method is applied to three examples including two case studies in finance and epidemiology. The results confirm the convergence of the approach for estimating the sensitivity indices even with the presence of strong heteroskedasticity and small signal-to-noise ratio.