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Efficient Nonparametric Conformal Prediction Regions
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We investigate and extend the conformal prediction method due to Vovk,Gammerman and Shafer (2005) to construct nonparametric prediction regions. These regions have guaranteed distribution free, finite sample coverage, without any assumptions on the distribution or the bandwidth. Explicit convergence rates of the loss function are established for such regions under standard regularity conditions. Approximations for simplifying implementation and data driven bandwidth selection methods are also discussed. The theoretical properties of our method are demonstrated through simulations.
Forward citations
Cited by 3 Pith papers
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On Optimal Data Splitting for Split Conformal Prediction
Derives analytical characterizations of the length-optimal training-calibration split ratio for split conformal prediction in general settings and specific regression models.
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A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Pith review generated a malformed one-line summary.
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Theoretical Foundations of Conformal Prediction
The book curates and presents proofs of important existing results in conformal prediction in a unified pedagogical format with illustrations.
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