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Cross‐estimation for decision selection

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  • Xinyue Gu
  • Bo Li

Abstract

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.

Suggested Citation

  • Xinyue Gu & Bo Li, 2020. "Cross‐estimation for decision selection," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(5), pages 932-958, September.
    • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:5:p:932-958
    DOI: 10.1002/asmb.2542
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    References listed on IDEAS

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    1. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    2. Yang Y., 2001. "Adaptive Regression by Mixing," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 574-588, June.
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