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Subsampling under distributional constraints

Author

Listed:
  • Florian Combes

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, AMU - Aix Marseille Université)

  • Ricardo Fraiman

    (CMAT - Centro de Matemática [Montevideo] - UDELAR - Universidad de la República de Uruguay = University of the Republic of Uruguay [Montevideo])

  • Badih Ghattas

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, AMU - Aix Marseille Université)

Abstract

Some complex models are frequently employed to describe physical and mechanical phenomena. In this setting, we have an input in a general space, and an output where is a very complicated function, whose computational cost for every new input is very high, and may be also very expensive. We are given two sets of observations of , and of different sizes such that only is available. We tackle the problem of selecting a subset of smaller size on which to run the complex model , and such that the empirical distribution of is close to that of . We suggest three algorithms to solve this problem and show their efficiency using simulated datasets and the Airfoil self‐noise data set.

Suggested Citation

  • Florian Combes & Ricardo Fraiman & Badih Ghattas, 2024. "Subsampling under distributional constraints," Post-Print hal-04742977, HAL.
    • Handle: RePEc:hal:journl:hal-04742977
    DOI: 10.1002/sam.11661
    Note: View the original document on HAL open archive server: https://hal.science/hal-04742977v1
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    References listed on IDEAS

    as
    1. HaiYing Wang & Min Yang & John Stufken, 2019. "Information-Based Optimal Subdata Selection for Big Data Linear Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 393-405, January.
    2. HaiYing Wang & Rong Zhu & Ping Ma, 2018. "Optimal Subsampling for Large Sample Logistic Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 829-844, April.
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