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Journal Articles Computational Statistics Year : 2024

Random Forest based Qantile Oriented Sensitivity Analysis indices estimation

Abstract

We propose a random forest based estimation procedure for Quantile-Oriented Sensitivity Analysis—QOSA. In order to be efficient, a cross-validation step on the leaf size of trees is required. Our full estimation procedure is tested on both simulated data and a real dataset. Our estimators use either the bootstrap samples or the original sample in the estimation. Also, they are either based on a quantile plug-in procedure (the R-estimators) or on a direct minimization (the Q-estimators). This leads to 8 different estimators which are compared on simulations. From these simulations, it seems that the estimation method based on a direct minimization is better than the one plugging the quantile. This is a significant result because the method with direct minimization requires only one sample and could therefore be preferred.
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Dates and versions

hal-03151021 , version 1 (24-02-2021)
hal-03151021 , version 2 (21-01-2024)
hal-03151021 , version 3 (05-05-2024)

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Kévin Elie-Dit-Cosaque, Véronique Maume-Deschamps. Random Forest based Qantile Oriented Sensitivity Analysis indices estimation. Computational Statistics, 2024, ⟨10.1007/s00180-023-01450-5⟩. ⟨hal-03151021v3⟩
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