Variance-based sensitivity analysis in the presence of correlated input variables
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913463074291712 |
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| author | Most, Thomas |
| author_facet | Most, Thomas |
| contents | In this paper we propose an extension of the classical Sobol' estimator for the estimation of variance based sensitivity indices. The approach assumes a linear correlation model between the input variables which is used to decompose the contribution of an input variable into a correlated and an uncorrelated part. This method provides sampling matrices following the original joint probability distribution which are used directly to compute the model output without any assumptions or approximations of the model response function. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04933 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Variance-based sensitivity analysis in the presence of correlated input variables Most, Thomas Methodology Machine Learning Statistics Theory In this paper we propose an extension of the classical Sobol' estimator for the estimation of variance based sensitivity indices. The approach assumes a linear correlation model between the input variables which is used to decompose the contribution of an input variable into a correlated and an uncorrelated part. This method provides sampling matrices following the original joint probability distribution which are used directly to compute the model output without any assumptions or approximations of the model response function. |
| title | Variance-based sensitivity analysis in the presence of correlated input variables |
| topic | Methodology Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2408.04933 |