报告题目：Online Nonparametric Models for Streaming Data

报 告 人：杨莹 博士后

报告人所在单位：中国科学院数学与系统科学研究院

报告日期：2024-01-05

报告时间：10:20-11:00

报告地点：光华东主楼2201

报告摘要：

Online learning and modeling has attracted considerable interest due to increasingly available data in streaming manner. Nonparametric models, although flexible, have seen limited use in online settings due to their data-driven nature and high computational demands. We introduce an innovative online method for dynamically updating local polynomial regression estimates. Our approach decomposes kernel-type estimates into two sufficient statistics and approximates future optimal bandwidths with a dynamic candidate sequence. We establish asymptotic normality and efficiency lower bounds for online estimation, shedding light on the trade-off between accuracy and computational cost driven by the bandwidth sequence length. This idea extends to general nonlinear optimization problems, where we propose an online smoothing backfitting method for generalized additive models with local linear estimation. We investigate statistical and algorithmic convergence and provide a framework for balancing estimation and computation performance. Our dynamic candidate bandwidth method is also adaptable to complex structural data such as functional data. Simulations and real data examples are provided to support the usefulness of the proposed method.