时间  Datetime
2021-06-17 14:00 — 15:30
地点  Venue
腾讯会议 APP3()
报告人  Speaker
许王莉
单位  Affiliation
中国人民大学
邀请人  Host
王成
备注  remarks
腾讯会议 ID：872 769 098 会议时间：2021/06/17 14:00-15:30
报告摘要  Abstract

This work is concerned with the estimation problem of linear model when the sample size is extremely large and the data dimension can vary with the sample size. In this setting, the least square estimator based on full data is not feasible with limited computational resources. Many existing methods for this problem are based on sketching technique. We derive fine-grained lower bounds of the conditional mean squared error for sketching methods. For sampling methods, our lower bound provides an attainable optimal convergence rate. We propose a new sketching method based on data averaging. The proposed method reduces the original data to a few averaged observations. These averaged observations still satisfy the linear model and are used to estimate the regression coefficients. The asymptotic behavior of the proposed estimation procedure is studied. Our theoretical results show that the proposed method can achieve a faster convergence rate than the optimal convergence rate for sampling methods. Theoretical and numerical results show that the proposed estimator has good statistical performance as well as low computational cost.
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