Reliable error estimation for Sobol’ indices
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In the field of sensitivity analysis, Sobol’ indices are sensitivity measures widely used to assess the importance of inputs of a model to its output. The estimation of these indices is often performed through Monte Carlo or quasi-Monte Carlo methods. A notable method is the replication procedure that estimates first-order indices at a reduced cost in terms of number of model evaluations. An inherent practical problem of this estimation is how to quantify the number of model evaluations needed to ensure that estimates satisfy a desired error tolerance. This article addresses this challenge by proposing a reliable error bound for first-order and total effect Sobol’ indices. Starting from the integral formula of the indices, the error bound is defined in terms of the discrete Walsh coefficients of the different integrands. We propose a sequential estimation procedure of Sobol’ indices using the error bound as a stopping criterion. The sequential procedure combines Sobol’ sequences with either Saltelli’s strategy to estimate both first-order and total effect indices, or the replication procedure to estimate only first-order indices.
KeywordsSobol’ index Error bound Sequential method Quasi-Monte Carlo
Mathematics Subject Classification49Q12 62L12 65R10
The authors thank Fred J. Hickernell and Clémentine Prieur for initiating this collaborative work, and Elise Arnaud for her proofreading. The authors are grateful to Stephen Joe, Frances Y. Kuo and Art B. Owen for their helpful answers and suggestions. The authors also thank the associate editor and the two anonymous reviewers for their helpful suggestions and comments which substantially improved the quality of this paper.
- Gilquin, L., Arnaud, E., Monod, H., Prieur, C.: Recursive estimation procedure of Sobol’ indices based on replicated designs. Preprint available at https://hal.inria.fr/hal-01291769 (2016)
- Hickernell, F.J., Jiménez Rugama, L.A., Li, D.: Adaptive quasi-Monte Carlo methods for cubature. Preprint available at https://arxiv.org/pdf/1702.01491.pdf (2017)
- McKay, M.D.: Evaluating prediction uncertainty, Los Alamos National Laboratory Report NUREG/CR- 6311, LA-12915-MS. (1995)Google Scholar