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arXiv 1702.00708math.OCmath.STstat.TH
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集值函数的统计:逆近似Optimization的应用

Statistics with Set-Valued Functions: Applications to Inverse Approximate Optimization

Anil Aswani

摘要:

大部分统计学依赖于四个关键要素:大数定律、运算随机收敛的微积分、中心极限定理和构造局部近似的框架。这些元素对于向量空间中的对象是很好理解的(例如,点或函数);然而,许多统计理论并不直接转化为集合,因为它们不形成向量空间。基于随机集的概率论,本文利用变分分析开发了集值函数统计的运算工具。这些工具首先应用于非参数估计(集值函数的核回归)。第二个应用是逆近似优化问题,其中观察到最优化问题的近似解(被噪声破坏),然后用于估计解的次优性量和生成解的最优化问题的参数。我们发现,以前的方法来解决这个问题是统计不一致的数据被噪声破坏时,而我们的方法是一致的,在温和的条件下。

英文摘要:

Much of statistics relies upon four key elements: a law of large numbers, a calculus to operationalize stochastic convergence, a central limit theorem, and a framework for constructing local approximations. These elements are well-understood for objects in a vector space (e.g., points or functions); however, much statistical theory does not directly translate to sets because they do not form a vector space. Building on probability theory for random sets, this paper uses variational analysis to develop operational tools for statistics with set-valued functions. These tools are first applied to nonparametric estimation (kernel regression of set-valued functions). The second application is to the problem of inverse approximate optimization, in which approximate solutions (corrupted by noise) to an optimization problem are observed and then used to estimate the amount of suboptimality of the solutions and the parameters of the optimization problem that generated the solutions. We show that previous approaches to this problem are statistically inconsistent when the data is corrupted by noise, whereas our approach is consistent under mild conditions.

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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