Evaluation of MARS modeling technique for sensitivity analysis of model output

By Davood Shahsavani, Stefano Tarantola, Marco Ratto. 2010

20 citations recorded by the source corpus.

Abstract Sensitivity analysis of computer models can typically require a large number of model runs. When these models are computationally expensive to run, it may be advantageous to invest in computationally cheaper surrogate models (emulators or meta-models) that can provide almost the same output as the original model and estimate the sensitivity indices for each input. In this abstract the MARS method is used to mimic the behavior of a nonlinear and non-additive test function. The results show that, overall, MARS provides acceptable estimates of total sensitivity indices at a much lower cost than using only runs of the original model.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Wei Gong
2. Chen Wang
3. Qingyun Duan
4. Zhenhua Di
5. Aizhong Ye
6. Chiyuan Miao

In Management Science and Operations Research

1. Wei Gong
2. Chen Wang
3. Qingyun Duan
4. Zhenhua Di
5. Aizhong Ye
6. Chiyuan Miao

In Statistics and Probability

1. Yongjiu Dai
2. Charles H. Tong
3. Wei Gong
4. Chen Wang
5. Jianduo Li
6. Qingyun Duan
7. Zhenhua Di
8. Aizhong Ye

Citations in the corpus

• An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “¼ ðx1; x2;…; xpÞ is the vector of inputs, Bj is the jth basis function, which can be a single spline function or a product of two or more basis function, and the coefficients bjs are estimated by minimizing the sum of squared residuals (Shahsavani et al., 2010).”
• Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis (2013, methodology): “The MARS method (Friedman, 1991; Shahsavani et al., 2010) is an extension of the regression tree method.”
• Variance-based sensitivity analysis of model outputs using surrogate models (2011, unknown): “The function that has already been used in the context of sensitivity analysis (Shahsavani et al., 2010), is defined as follows y ¼ Yk i¼1 f ðxiÞ; f ðxiÞ ¼ b 0:25þ 1 2p arcsinðcosð2pðxi þ cÞÞÞ a (1) where 0 xi 1, k is the number of inputs, a ¼ 0:25, c ¼ 0, and b ¼ ðaþ 1Þ2a.”
• An Intercomparison of Sampling Methods for Uncertainty Quantification of Environmental Dynamic Models (2015, methodology): “It can also be used as a surrogate model (Crino et al., 2007) as well as a sensitivity analysis method (Shahsavani et al., 2010).”