学术报告(林振华 6.26)

Optimal One-pass Nonparametric Estimation Under Memory Constraint

发布人:杨晓静 发布日期:2023-06-02
主题
Optimal One-pass Nonparametric Estimation Under Memory Constraint
活动时间
-
活动地址
数学楼 415
主讲人
林振华校长青年教授 新加坡国立大学
主持人
黄辉

Abstract: 

For nonparametric regression in the streaming setting, where data constantly flow in and require real-time analysis, a main challenge is that data are cleared from the computer system once processed due to limited computer memory and storage. We tackle the challenge by proposing a novel one-pass estimator based on penalized orthogonal basis expansions and developing a general framework to study the interplay between statistical efficiency and memory consumption of estimators. We show that, the proposed estimator is statistically optimal under memory constraint, and has asymptotically minimal memory footprints among all one-pass estimators of the same estimation quality. Numerical studies demonstrate that the proposed one-pass estimator is nearly as efficient as its non-streaming counterpart that has access to all historical data.