A Distributed Estimator based on a One-Step Approach
报告人:
Dr. Xiaoming Huo
Xiaoming Huo is a professor at the Stewart School of Industrial & Systems Engineering at Georgia Tech. Dr.
报告时间:
2016-05-30 10:00
报告地点:
舜德楼北510
主办单位:
清华大学工业工程系
简介:
Distributed statistical inference has recently attracted enormous attention. Many existing work focuses on the averaging estimator. We propose a one-step approach to enhance a simple-averaging based distributed estimator. We derive the corresponding asymptotic properties of the newly proposed estimator. We find that the proposed one-step estimator enjoys the same asymptotic properties as the centralized estimator. The proposed one-step approach merely requires one additional round of communication in relative to the averaging estimator; so the extra communication burden is insignificant. In finite sample cases, numerical examples show that the proposed estimator outperforms the simple averaging estimator with a large margin in terms of the mean squared errors. A potential application of the one-step approach is that one can use multiple machines to speed up large scale statistical inference with little compromise in the quality of estimators. The proposed method becomes more valuable when data can only be available at distributed machines with limited communication bandwidth. This talk is based on joint work with Cheng Huang. A related manuscript can be found at http://arxiv.org/abs/1511.01443.
