题 目: Dimension Reduction in Regression and Its Application to Macroeconomics

汇报人：Jie Yang 杨杰 （University of Illinois at Chicago）

时 间：2012年5月18日上午10：00（周五）

地 点：拉斯维加斯9888新楼酒店一层 K03 教室

提要: In this talk, we introduce a powerful statistical method called dimension reduction in regression, along with selected examples of its application, especially in Macroeconomics. The regular regression of a univariate response on a p-dimensional predictor vector x is to make inference on y given x. The goal of dimension reduction in regression is to find a d-dimensional predictor vector z, which is a smaller number of linear combinations of x, such that x can be replaced with z to make inference on y. Unlike the widely used method principal component analysis (PCA) in economics and other fields, the dimension reduction in regression takes into account the information contained in y during reducing the dimension of x, which is more efficient and powerful than the PCA method.

演讲人简介：杨杰博士，芝加哥大学统计学博士（2006），南开大学利用数学博士（2001），美国伊利诺伊大学芝加哥分校助理教授。

迎接感兴致的教员和同学参与！