机构设计中的特征选择
Feature Selection by a Mechanism Design
摘要：
在构建计量经济学或统计模型时，我们从许多候选者中挑选相关的特征或变量。建立了一个联盟博弈模型来研究选择问题，其中参与者是候选人，支付函数是所有可能建模场景中的性能度量。因此，在理论上，一个不相关的特征相当于游戏中的一个虚拟玩家，它对所有建模情况都没有贡献。零均值贡献假设检验是判断特征是否无关的准则。在我们的机制设计中，最终目标完美地匹配了预期的模型性能和预期的个体边际效应之和。在所有建模机会中的一类无信息可能性中，匹配方程导致每个特征的特定估值。在估计估值及其标准差之后，如果其估值与零没有显著差异，则我们丢弃任何候选特征。在仿真研究中，我们的新方法显着优于几个流行的方法在实践中使用，其精度是强大的支付函数的选择。
英文摘要：
In constructing an econometric or statistical model, we pick relevant features or variables from many candidates. A coalitional game is set up to study the selection problem where the players are the candidates and the payoff function is a performance measurement in all possible modeling scenarios. Thus, in theory, an irrelevant feature is equivalent to a dummy player in the game, which contributes nothing to all modeling situations. The hypothesis test of zero mean contribution is the rule to decide a feature is irrelevant or not. In our mechanism design, the end goal perfectly matches the expected model performance with the expected sum of individual marginal effects. Within a class of noninformative likelihood among all modeling opportunities, the matching equation results in a specific valuation for each feature. After estimating the valuation and its standard deviation, we drop any candidate feature if its valuation is not significantly different from zero. In the simulation studies, our new approach significantly outperforms several popular methods used in practice, and its accuracy is robust to the choice of the payoff function.
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。
