Date: 2022-11-18

Time: 15:30-16:30 (Montreal time)

https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09

Meeting ID: 834 3668 6293

Passcode: 12345

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.

Speaker

Zhenhua Lin is a Presidential Young Professor in the Department of Statistics and Data Science at National University of Singapore. He is also an Affiliated Faculty in the Institute of Data Science. His research interests include Functional data analysis non-Euclidean data analysis, high-dimensional data analysis, statistics under non-statistical constraints. He is the Associate Editor of Bernoulli Journal.

https://blog.nus.edu.sg/zhenhua/

McGill Statistics Seminar schedule: https://mcgillstat.github.io/