Subsampling under distributional constraints

Florian Combes, Ricardo Fraiman and Badih Ghattas
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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 [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

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Abstract: 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.

Date: 2024-02-09
Note: View the original document on HAL open archive server: https://hal.science/hal-04742977v1
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Published in Statistical Analysis and Data Mining, 2024, 17 (1), ⟨10.1002/sam.11661⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04742977

DOI: 10.1002/sam.11661

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