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Random forest based quantile-oriented sensitivity analysis indices estimation

Author

Listed:
  • Kévin Elie-Dit-Cosaque

    (ICJ UMR5208, CNRS, Ecole Centrale de Lyon, INSA Lyon, Université Jean Monnet
    SCOR)

  • Véronique Maume-Deschamps

    (ICJ UMR5208, CNRS, Ecole Centrale de Lyon, INSA Lyon, Université Jean Monnet)

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.

Suggested Citation

  • Kévin Elie-Dit-Cosaque & Véronique Maume-Deschamps, 2024. "Random forest based quantile-oriented sensitivity analysis indices estimation," Computational Statistics, Springer, vol. 39(4), pages 1747-1777, June.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:4:d:10.1007_s00180-023-01450-5
    DOI: 10.1007/s00180-023-01450-5
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