Random forest based quantile-oriented sensitivity analysis indices estimation

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...

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Bibliographic Details
Published in:Computational statistics 2024-06, Vol.39 (4), p.1747-1777
Main Authors: Elie-Dit-Cosaque, Kévin, Maume-Deschamps, Véronique
Format: Article
Language:English
Subjects:
Algorithms
Bandwidths
Datasets
Economic Theory/Quantitative Economics/Mathematical Methods
Estimators
Mathematical functions
Mathematics and Statistics
Optimization
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Quantiles
Random variables
Sensitivity analysis
Simulation
Statistics
Citations: Items that this one cites
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Summary: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.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-023-01450-5