Uncertainty quantification for honest regression trees

Suofei Wu, Jan Hannig and Thomas C.M. Lee

Computational Statistics & Data Analysis, 2022, vol. 167, issue C

Abstract: A new method is developed for quantifying the uncertainties of the estimates and predictions produced by honest random forests. This new method is based on the generalized fiducial methodology, and provides a fiducial density function that measures how likely each single honest tree is the true model. With such a density function, estimates and predictions, as well as their confidence/prediction intervals, can be obtained. The promising empirical properties of the proposed method are demonstrated by numerical comparisons with several state-of-the-art methods, and by applications to a few real data sets. Lastly, the proposed method is theoretically backed up by an asymptotic guarantee.

Keywords: Bootstrap; Confidence intervals; Generalized fiducial inference; Jackknife; Prediction intervals (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:167:y:2022:i:c:s0167947321002115

DOI: 10.1016/j.csda.2021.107377

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