Random forest based quantile-oriented sensitivity analysis indices estimation

Kévin Elie-Dit-Cosaque () and Véronique Maume-Deschamps ()
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Kévin Elie-Dit-Cosaque: ICJ UMR5208, CNRS, Ecole Centrale de Lyon, INSA Lyon, Université Jean Monnet
Véronique Maume-Deschamps: ICJ UMR5208, CNRS, Ecole Centrale de Lyon, INSA Lyon, Université Jean Monnet

Computational Statistics, 2024, vol. 39, issue 4, No 3, 1747-1777

Abstract: 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.

Keywords: Quantile-oriented sensitivity analysis; Random forest; Cross validation; Out-of-bag samples (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-023-01450-5

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