Optimum Statistical Estimation with Strategic Data Sources
Optimum Statistical Estimation with Strategic Data Sources
复制标题
利用战略数据源进行最佳统计估计
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Christos H. Papadimitriou
中科院分区：
文献类型：
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作者：
Yang Cai;C. Daskalakis;Christos H. Papadimitriou
我们提出了一种最优机制，用于向线性回归等统计估计的数据源提供货币激励，从而在支付和估计误差最小化的意义上以低成本提供高质量的数据。该机制适用于广泛的估计量，包括线性和多项式回归、核回归，以及在一些附加假设下的岭回归。它还概括了几个目标，包括在预算限制下尽量减少估计误差。除了我们对回归问题的具体结果外，我们还提供了一个机制设计框架，通过它来设计和分析统计估计器，这些估计器的例子是由工人提供的，用于标记这些例子的费用。
We propose an optimum mechanism for providing monetary incentives to the data sources of a statistical estimator such as linear regression, so that high quality data is provided at low cost, in the sense that the sum of payments and estimation error is minimized. The mechanism applies to a broad range of estimators, including linear and polynomial regression, kernel regression, and, under some additional assumptions, ridge regression. It also generalizes to several objectives, including minimizing estimation error subject to budget constraints. Besides our concrete results for regression problems, we contribute a mechanism design framework through which to design and analyze statistical estimators whose examples are supplied by workers with cost for labeling said examples.