English(EN) Limit Theory for Online Expectile Estimation under Short and Long Range Dependence

新的依赖数据在线期望分位数估计算法在arXiv上公布

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-14 04:00

一篇新发表在arXiv上的论文详细介绍了一种用于从依赖数据估计期望分位数的一次性、随机梯度算法。该研究解决了在线期望分位数估计的有限统计理论问题，特别是针对具有短期和长期依赖性的过程。研究提出了算法渐近行为的理论发现，包括短期依赖下的高斯极限以及长记忆过程的修正尺度，并通过数值实验进行了验证。 AI

影响为可能应用于涉及时间序列数据的AI/ML场景的统计方法提供了理论进展。

排序理由详细介绍新统计算法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 2 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍新统计算法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Younghun Lee, Jake Zummo, Wei Biao Wu · 2026-10-14 04:00

短长程依赖下在线期望分位数估计的极限理论

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…

本文如何被排名

报道来源 [1]

短长程依赖下在线期望分位数估计的极限理论

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