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Multivariate sensitivity analysis for a large-scale climate impact and adaptation model

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Multivariate sensitivity analysis for a large-scale climate impact and adaptation model. / Oyebamiji, Oluwole; Nemeth, Christopher John; Harrison, Paula et al.
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 72, No. 3, 13.06.2023, p. 770-808.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Oyebamiji, O, Nemeth, CJ, Harrison, P, Dunford, R & Cojocaru, G 2023, 'Multivariate sensitivity analysis for a large-scale climate impact and adaptation model', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 72, no. 3, pp. 770-808. https://doi.org/10.1093/jrsssc/qlad032

APA

Oyebamiji, O., Nemeth, C. J., Harrison, P., Dunford, R., & Cojocaru, G. (2023). Multivariate sensitivity analysis for a large-scale climate impact and adaptation model. Journal of the Royal Statistical Society: Series C (Applied Statistics), 72(3), 770-808. https://doi.org/10.1093/jrsssc/qlad032

Vancouver

Oyebamiji O, Nemeth CJ, Harrison P, Dunford R, Cojocaru G. Multivariate sensitivity analysis for a large-scale climate impact and adaptation model. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2023 Jun 13;72(3):770-808. Epub 2023 May 15. doi: 10.1093/jrsssc/qlad032

Author

Oyebamiji, Oluwole ; Nemeth, Christopher John ; Harrison, Paula et al. / Multivariate sensitivity analysis for a large-scale climate impact and adaptation model. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2023 ; Vol. 72, No. 3. pp. 770-808.

Bibtex

@article{fa5a4244afa84e53be0a1ae139fd2eec,
title = "Multivariate sensitivity analysis for a large-scale climate impact and adaptation model",
abstract = "We apply a new efficient methodology for Bayesian global sensitivity analysis for large-scale multivariate data. A multivariate Gaussian process is used as a surrogate model to replace the expensive computer model. To improve the computational efficiency and performance of the model, compactly supported correlation functions are used. The goal is to generate sparse matrices, which give crucial advantages when dealing with large data sets. The method was applied to multivariate data from the IMPRESSIONS Integrated Assessment Platform version 2. Our empirical results on Integrated Assessment Platform version 2 data show that the proposed methods are efficient and accurate for global sensitivity analysis of complex models.",
keywords = "Bayesian methods, compactly supported correlation function, Gaussian process, robust adaptive MCMC, sensitivity analysis",
author = "Oluwole Oyebamiji and Nemeth, {Christopher John} and Paula Harrison and Rob Dunford and George Cojocaru",
year = "2023",
month = jun,
day = "13",
doi = "10.1093/jrsssc/qlad032",
language = "English",
volume = "72",
pages = "770--808",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "3",

}

RIS

TY - JOUR

T1 - Multivariate sensitivity analysis for a large-scale climate impact and adaptation model

AU - Oyebamiji, Oluwole

AU - Nemeth, Christopher John

AU - Harrison, Paula

AU - Dunford, Rob

AU - Cojocaru, George

PY - 2023/6/13

Y1 - 2023/6/13

N2 - We apply a new efficient methodology for Bayesian global sensitivity analysis for large-scale multivariate data. A multivariate Gaussian process is used as a surrogate model to replace the expensive computer model. To improve the computational efficiency and performance of the model, compactly supported correlation functions are used. The goal is to generate sparse matrices, which give crucial advantages when dealing with large data sets. The method was applied to multivariate data from the IMPRESSIONS Integrated Assessment Platform version 2. Our empirical results on Integrated Assessment Platform version 2 data show that the proposed methods are efficient and accurate for global sensitivity analysis of complex models.

AB - We apply a new efficient methodology for Bayesian global sensitivity analysis for large-scale multivariate data. A multivariate Gaussian process is used as a surrogate model to replace the expensive computer model. To improve the computational efficiency and performance of the model, compactly supported correlation functions are used. The goal is to generate sparse matrices, which give crucial advantages when dealing with large data sets. The method was applied to multivariate data from the IMPRESSIONS Integrated Assessment Platform version 2. Our empirical results on Integrated Assessment Platform version 2 data show that the proposed methods are efficient and accurate for global sensitivity analysis of complex models.

KW - Bayesian methods

KW - compactly supported correlation function

KW - Gaussian process

KW - robust adaptive MCMC

KW - sensitivity analysis

U2 - 10.1093/jrsssc/qlad032

DO - 10.1093/jrsssc/qlad032

M3 - Journal article

VL - 72

SP - 770

EP - 808

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 3

ER -