English(EN) Functional linear regression from sparse to dense designs: a pooling-ridge method and minimax optimality

新的汇聚岭方法优化稀疏数据的函数线性回归

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

研究人员引入了一种名为汇聚岭估计的新统计方法，以解决离散观测数据函数线性回归的长期挑战。该方法结合了汇聚策略和再生核希尔伯特空间（RKHS）方法，在从稀疏到稠密设计的各种采样密度下实现最优预测风险。该方法适用于标量对函数和函数对函数回归模型，揭示了受采样频率影响的不同相变。 AI

影响引入了一种新颖的函数数据分析统计方法，有可能提高机器学习模型在时间序列或基于函数的数据上的性能。

排序理由该集群包含一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv stat.ML TIER_1 English(EN) · Shunxing Yan, Fang Yao · 2026-08-27 04:00

从稀疏到稠密设计的函数线性回归：一种汇聚岭方法和极小极大最优性

arXiv:2608.25468v1 Announce Type: cross Abstract: Functional data analysis is an important statistical field that treats data as random functions. In practice, the random functions are often not fully observed but instead measured at discrete times. While simpler problems, such a…