Empirical Uniform Bounds For Heteroscedastic Metamodeling
Empirical Uniform Bounds For Heteroscedastic Metamodeling
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DOI：
10.1109/wsc57314.2022.10015525
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Yutong Zhang;Xi Chen
中科院分区：
文献类型：
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作者：
Yutong Zhang;Xi Chen
本文在考虑噪声方差估计影响的基础上，提出了基于点方差估计和基于元模型的异方差元建模经验均匀界。数值结果表明，现有的标称均匀界需要较多的设计点和较高的重复次数才能达到规定的目标覆盖水平。另一方面，基于元模型的经验边界在经验同时覆盖概率和边界宽度方面优于名义边界和其他竞争边界，特别是当模拟预算较小时。然而，基于点方差估计的经验界宽度较大，相对保守。当预算足够大以至于异方差的影响很低时，两个经验边界的表现都接近名义边界的表现。
This paper proposes pointwise variance estimation-based and metamodel-based empirical uniform bounds for heteroscedastic metamodeling based on the state-of-the-art nominal uniform bound available from the literature by considering the impact of noise variance estimation. Numerical results show that the existing nominal uniform bound requires a relatively large number of design points and a high number of replications to achieve a prescribed target coverage level. On the other hand, the metamodel-based empirical bound outperforms the nominal bound and other competing bounds in terms of empirical simultaneous coverage probability and bound width, especially when the simulation budget is small. However, the pointwise variance estimation-based empirical bound is relatively conservative due to its larger width. When the budget is sufficiently large so that the impact of heteroscedasticity is low, both empirical bounds' performance approaches that of the nominal bound.