讲座内容：

主讲人介绍：霍晓明教授现工作于佐治亚理工学院工业与系统工程专业的斯图尔特学院，其主要研究研究领域为：Analytics and Big Data、Economic Decision Analysis、Statistics和Supply Chain Engineering。霍晓明教授已在Journal of the American Statistical Association、Annals of Statistics、Statistica Sinica等国际顶级期刊发表数十篇文章。

讲座内容简介：

This talk will have two parts: Analytics and Statistics.

In the first part, I will give an overview of business analytics, discuss its research problems, as well as related research topics. I will review components of business analytics that I perceive as critical. I will describe some projects that I have done in the past, though they are not necessarily from “business.” I will give my thoughts on how to carry out relevant research.

The second part is about a particular statistical problem that I’ve worked on recently, namely distributed inference. 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.