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New statistical framework enhances prediction intervals for polynomial chaos expansions

By PulseAugur Editorial · [1 sources] ·

A new statistical framework called Locally Adaptive Conformal Prediction has been developed for polynomial chaos expansions. This method normalizes non-conformity scores using an auxiliary polynomial chaos expansion trained on model residuals. The framework ensures coverage guarantees for adaptive intervals under specific conditions and can provide statistically efficient intervals that adapt to local prediction uncertainty, particularly in high-uncertainty regions. AI

IMPACT Enhances statistical efficiency and uncertainty adaptation in predictive modeling.

RANK_REASON The cluster contains an academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New statistical framework enhances prediction intervals for polynomial chaos expansions

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The cluster contains an academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Dimitrios Loukrezis, Arthur Hatstatt, Bruno Sudret, Dimitris G. Giovanis ·

    Locally Adaptive Conformal Prediction for Polynomial Chaos Expansions

    arXiv:2611.09579v1 Announce Type: cross Abstract: This work develops a locally adaptive jackknife+ conformal prediction framework for polynomial chaos expansions. Therein, the non-conformity scores are normalized with an auxiliary polynomial chaos expansion trained on the log-tra…

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