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New algorithm for online expectile estimation from dependent data detailed on arXiv

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv details a one-pass, stochastic-gradient algorithm for estimating expectiles from dependent data. The research addresses the limited statistical theory for online expectile estimation, particularly for processes with short and long-range dependence. The study presents theoretical findings on the asymptotic behavior of the algorithm, including Gaussian limits under short-range dependence and a modified scale for long-memory processes, supported by numerical experiments. AI

IMPACT Provides theoretical advancements for statistical methods potentially applicable in AI/ML contexts involving time-series data.

RANK_REASON Academic paper detailing a new statistical algorithm. [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 algorithm for online expectile estimation from dependent data detailed on arXiv

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Academic paper detailing a new statistical algorithm. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Younghun Lee, Jake Zummo, Wei Biao Wu ·

    Limit Theory for Online Expectile Estimation under Short and Long Range Dependence

    arXiv:2610.16030v1 Announce Type: new Abstract: Expectiles are tail-sensitive distributional summaries with important applications in financial risk measurement and forecasting, yet statistical theory for their online estimation from dependent data remains limited. We study a one…

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