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Technical note: Sufficient operational statistics

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Listed:
  • Justin Jia
  • Elena Katok

Abstract

The decision in a data‐driven decision‐making problem is generally a high‐dimensional function of data. When can the decision be reduced to a single‐dimensional function of a statistic? This study addresses this question based on the operational statistics literature. The study introduces the notion of sufficient operational statistics and derives the factorization theorem for identifying such statistics. Further, the study proposes a solution procedure based on the statistics and derives the finite‐sample performance bound of the proposed solution.

Suggested Citation

  • Justin Jia & Elena Katok, 2022. "Technical note: Sufficient operational statistics," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2429-2437, June.
    • Handle: RePEc:bla:popmgt:v:31:y:2022:i:6:p:2429-2437
    DOI: 10.1111/poms.13678
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    References listed on IDEAS

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    1. Gah-Yi Ban & Cynthia Rudin, 2019. "The Big Data Newsvendor: Practical Insights from Machine Learning," Operations Research, INFORMS, vol. 67(1), pages 90-108, January.
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