A relevance vector regression based metamodeling approach for complex system analysis

Wu, Bing; Chen, Ling; Hu, Zhiwei; Zhang, Wenqiong; Jia-hong, Liang

doi:10.1109/asc-icsc.2008.4675433

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…Recent works in this area include (Kleijnen and Deflandre 2006), (Wu, Chen, Hu, Zhang, and Liang 2008), (Kleijnen 2009), (Ankenman, Nelson, and Staum 2010), (Khuri and Mukhopadhyay 2010), (Yin, Ng, and Ng 2011), (Razavi, Tolson, and Burn 2012), (Wei, Wu, and Chen 2012), and (Zhao, Yue, Liu, Gao, and Zhang 2014), to name just a few. Kleijnen (2009) presented a review of the Kriging metamodel.…”

Section: Metamodelingmentioning

confidence: 98%

Application of metamodeling to the valuation of large variable annuity portfolios

Gan¹

2015

2015 Winter Simulation Conference (WSC)

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Variable annuities are long-term investment vehicles that have grown rapidly in popularity recently. One major feature of variable annuities is that they contain guarantees. The guarantees embedded in variable annuities are complex and the values of the guarantees cannot be obtained from closed-form formulas. Insurance companies rely heavily on Monte Carlo simulation to calculate the fair market values of the guarantees. Valuation and risk management of a large portfolio of variable annuities are a big challenge to insurance companies because the Monte Carlo simulation model is very time consuming. In this paper, we propose to use a metamodeling approach to speed up the valuation of large portfolios of variable annuities. Our numerical results show that the metamodeling approach can reduce the runtime significantly and produce accurate approximations.

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Section: Metamodelingmentioning

confidence: 98%

Application of metamodeling to the valuation of large variable annuity portfolios

Gan¹

2015

2015 Winter Simulation Conference (WSC)

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“…com/ Zaixu Cui/ Patte rn_ Regre ssion_ Clean). RVR has been demonstrated as an appropriate machine learning approach for complex information simulation with good robustness and acceptable computational efficiency (Wu et al, 2008). RVR is a pattern recognition method that uses a full probabilistic Bayesian inference to obtain sparse regression models.…”

Section: The Multivariate Relevance Vector Regression (Rvr) Analysismentioning

confidence: 99%

Multivariate morphological brain signatures enable individualized prediction of dispositional need for closure

Chen

et al. 2021

Brain Imaging and Behavior

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Remaining useful life estimation using long short-term memory deep learning

Hsu

Jiang

2018

2018 IEEE International Conference on Applied System Invention (ICASI)

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A relevance vector regression based metamodeling approach for complex system analysis

Cited by 8 publications

References 12 publications

Application of metamodeling to the valuation of large variable annuity portfolios

Application of metamodeling to the valuation of large variable annuity portfolios

Multivariate morphological brain signatures enable individualized prediction of dispositional need for closure

Remaining useful life estimation using long short-term memory deep learning

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