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  "Description": "This new extreme conformal prediction framework provides\ninformative prediction intervals at the high-confidence levels\nfor which classical conformal methods fail. In applications\nwith potentially high-impact events, a very high level of\nconfidence is often required for predictions. If that level is\ntoo large relative to the amount of data used for calibration,\nclassical conformal methods provide infinitely wide, thus,\nuninformative prediction intervals. Our extreme conformal\nprocedure bridges extreme value statistics and conformal\nprediction to provide reliable and informative prediction\nintervals with high-confidence coverage, which can be\nconstructed using any black-box extreme quantile regression\nmethod. A weighted version of the approach can account for\nnonstationary data. The methodology was introduced in Pasche,\nLam, and Engelke (2026) <doi:10.1007/s10687-026-00536-9>.",
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