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- Title
Random forest based quantile-oriented sensitivity analysis indices estimation.
- Authors
Elie-Dit-Cosaque, Kévin; Maume-Deschamps, Véronique
- 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.
- Subjects
RANDOM forest algorithms; SENSITIVITY analysis; TREE size; QUANTILE regression
- Publication
Computational Statistics, 2024, Vol 39, Issue 4, p1747
- ISSN
0943-4062
- Publication type
Article
- DOI
10.1007/s00180-023-01450-5