Conformal prediction for robust deep nonparametric regression

Jingsen Kong (), Yiming Liu (), Guangren Yang () and Wang Zhou ()
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Jingsen Kong: Jinan University
Yiming Liu: Jinan University
Guangren Yang: Jinan University
Wang Zhou: National University of Singapore

Statistical Papers, 2025, vol. 66, issue 1, No 10, 36 pages

Abstract: Abstract Conformal prediction is a general method used to convert a point predictor into a prediction band. The accuracy of this prediction band is heavily reliant on the base estimator. This paper is to investigate the use of conformal prediction by least absolute deviation-based deep nonparametric regression. We demonstrate the consistency of the robust deep regression estimator under mild conditions, leading to the proposed prediction band exhibiting finite-sample marginal validity and asymptotic conditional validity. Through extensive simulation studies and a real-data example, we illustrate the benefits of conformal prediction for robust deep regression.

Keywords: Conformal prediction; Deep neural networks; Least absolute deviation; Robust regression (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00362-024-01631-4

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