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New method enhances quantile regression with multi-fidelity data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel two-stage method for multi-fidelity quantile regression, designed to improve the accuracy of quantile estimation when high-fidelity data is scarce. The approach utilizes a local quantile link, representing high-fidelity quantiles based on low-fidelity quantiles evaluated at a covariate-dependent level. This reformulation aims to simplify the estimation process by focusing on a smoother level function, with a correction step included for enhanced robustness. Theoretical analysis and experimental results on synthetic and real-world data demonstrate that this method can achieve faster convergence and more precise quantile estimates compared to using only high-fidelity data. AI

IMPACT Introduces a new statistical technique that could improve the accuracy of predictive models in data-scarce scenarios.

RANK_REASON This is a research paper published on arXiv detailing a new statistical methodology. [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 method enhances quantile regression with multi-fidelity data

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This is a research paper published on arXiv detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 Italiano(IT) · Yixiang Liu, Yao Zhang ·

    Multi-Fidelity Quantile Regression

    arXiv:2605.10406v2 Announce Type: replace-cross Abstract: High-fidelity (HF) data are often expensive to collect and therefore scarce, making conditional quantiles difficult to estimate accurately. We propose a two-stage, model-agnostic method for multi-fidelity quantile regressi…

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