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Data Science and Productivity Analytics

Editor

Listed:
  • Vincent Charles
    (University of Bradford)

  • Juan Aparicio
    (University Miguel Hernandez)

  • Joe Zhu
    (Worcester Polytechnic Institute)

Abstract

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Suggested Citation

  • Vincent Charles & Juan Aparicio & Joe Zhu (ed.), 2020. "Data Science and Productivity Analytics," International Series in Operations Research and Management Science, Springer, number 978-3-030-43384-0, December.
    • Handle: RePEc:spr:isorms:978-3-030-43384-0
    DOI: 10.1007/978-3-030-43384-0
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    Cited by:

    1. Valero-Carreras, Daniel & Aparicio, Juan & Guerrero, Nadia M., 2021. "Support vector frontiers: A new approach for estimating production functions through support vector machines," Omega, Elsevier, vol. 104(C).
    2. Esteve, Miriam & Aparicio, Juan & Rodriguez-Sala, Jesus J. & Zhu, Joe, 2023. "Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull," European Journal of Operational Research, Elsevier, vol. 304(2), pages 729-744.

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