We propose a data‐driven procedure, cross‐estimation for decision selection (CrEDS), to choose from an abundance of off‐the‐shelf statistical models or computer algorithms at a decision‐maker's disposal. CrEDS combines the ideas of cross‐validation (CV) and local smoothing, a nonparametric statistical technique. We demonstrate the power of CrEDS with five numerical experiments in inventory and revenue management problems, ranging from low to high dimensional and from exogenous to endogenous. We also conduct a case study using an auto‐lending data. CrEDS performs favorably compared to other existing selection criteria and provides a practical framework for a broad range of optimal decision selection problems.

中文翻译：

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决策选择的交叉估计

我们提出了一种数据驱动程序，即决策选择的交叉估计（Cr EDS），以便从决策者可以使用的大量现成的统计模型或计算机算法中进行选择。Cr EDS结合了交叉验证（CV）和非参数统计技术局部平滑的思想。我们通过五个数值实验来证明Cr EDS的强大功能，它们涉及库存和收益管理问题，涉及的范围从低到高，从外生到内生。我们还使用自动贷款数据进行了案例研究。Cr EDS与其他现有选择标准相比，表现出色，并且为广泛的最佳决策选择问题提供了实用的框架。