新的分析详细介绍了重尾噪声下的线性系统估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

研究人员开发了一种新的非渐近分析方法，用于估计向量自回归模型，特别适用于具有重尾噪声的系统。该研究建立了样本复杂度界限，表明估计误差受噪声维度和样本数量的影响。该方法已推广到各种噪声分布，包括次指数和次高斯分布，并应用于具有外生输入的自回归模型，证明了维度因子与模型阶数无关。 AI

影响这项研究推进了统计学习的理论理解，有可能提高处理噪声数据的模型的鲁棒性。

排序理由在arXiv上发表的学术论文，详细介绍了一种新的机器学习分析方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Xiaomian Yang, Sungho Shin · 2026-10-03 04:00

重尾噪声下线性系统的学习：单轨迹的非渐近分析

arXiv:2610.00637v1 Announce Type: new Abstract: We establish non-asymptotic sample complexity bounds for the least-squares estimation of vector autoregressive models for exponentially stable systems with heavy-tailed noise based on a single observed trajectory. By assuming i.i.d.…