讲座题目: Sufficient Dimension Folding for Regression Mean Function
主讲人: 对外经济贸易大学统计学院经济统计系 薛原(助理教授)
主讲人简介:
薛原, 2012获美国佐治亚大学统计学博士学位,师从殷向荣教授.研究方向为高维数据充分降维；数据区分与判别分析；数据挖掘与变量选择；高维数据计算等.曾获美国东南区统计协会Boyd Harshbarger poster奖, 美国统计师协会北卡罗来纳州分会AISC 2012 青年研究员奖, 佐治亚大学研究生院会议奖等各类奖项.并在统计学年会、统计跨学科国际会议、泛华统计统计协会主办的ICSA2013等国际会议上作受邀报告.
时间：2014年5月19日下午15:00点
地点：伟德bevictor中文版犀浦校区bevictor伟德官网会议室X2511
内容简介:
In this paper, we consider sufficient dimension folding for the regression mean function when predictors are matrix- or array-valued. We propose a new concept named central mean dimension folding subspace and its two local estimation methods: folded outer product of gradients estimation (folded-OPG) and folded minimum average variance estimation (folded-MAVE). We establish the asymptotic properties for folded-MAVE. A modified BIC criterion is used to determine the dimensions of the central mean dimension folding subspace. We evaluate the performances of the two local estimation methods by simulated examples and demonstrate the efficacy of folded-MAVE in finite samples. And in particular, we apply our methods to analyze a longitudinal study of primary biliary cirrhosis.
