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Randomized Sobol’ Sensitivity Indices

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Monte Carlo and Quasi-Monte Carlo Methods (MCQMC 2016)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 241))

Abstract

Classical Sobol’ sensitivity indices assume the distribution of a model’s parameters is known completely for a given model, but this is usually difficult to measure in practical problems. What is measurable is the distribution of parameters for a particular data set, and the Sobol’ indices can significantly vary as different data sets are used in the estimation of the parameter distributions. To address this issue, we introduce a hierarchical probabilistic framework where Sobol’ sensitivity indices are random variables. An ANOVA decomposition in this hierarchical framework is given. Some analytical examples and an application to interest rate modeling illustrate the use of the randomized Sobol’ indices framework.

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Acknowledgements

We thank Art Owen and the anonymous referee for their valuable comments that improved the paper.

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Authors and Affiliations

  1. Florida State University, Tallahassee, 32306, FL, USA

    David Mandel & Giray Ökten

Authors
  1. David Mandel
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  2. Giray Ökten
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Corresponding author

Correspondence to David Mandel .

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Editors and Affiliations

  1. Department of Statistics, Stanford University, Stanford, California, USA

    Art B. Owen

  2. Department of Management Science and Engineering, Stanford University, Stanford, California, USA

    Peter W. Glynn

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Mandel, D., Ökten, G. (2018). Randomized Sobol’ Sensitivity Indices. In: Owen, A., Glynn, P. (eds) Monte Carlo and Quasi-Monte Carlo Methods. MCQMC 2016. Springer Proceedings in Mathematics & Statistics, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-91436-7_22

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