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arXiv 1406.6985math.OC
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随机变分不等式正规映射的对称置信区域和置信区间

Symmetric confidence regions and confidence intervals for normal map formulations of stochastic variational inequalities

Shu Lu

摘要:

随机变分不等式(SVI)对一类具有数据不确定性的平衡问题进行建模,并且与随机最优化问题密切相关。SVI解通常由样本平均近似(SAA)问题的解来估计。研究了SVI的正态映射问题,基于SAA解的渐近分布,提出了一种构造正态映射解的渐近精确置信区域和置信区间的方法.置信区域是具有高概率的单椭球。我们还讨论了计算的同时和个人的置信区间。

英文摘要:

Stochastic variational inequalities (SVI) model a large class of equilibrium problems subject to data uncertainty, and are closely related to stochastic optimization problems. The SVI solution is usually estimated by a solution to a sample average approximation (SAA) problem. This paper considers the normal map formulation of an SVI, and proposes a method to build asymptotically exact confidence regions and confidence intervals for the solution of the normal map formulation, based on the asymptotic distribution of SAA solutions. The confidence regions are single ellipsoids with high probability. We also discuss the computation of simultaneous and individual confidence intervals.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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