统计与管理学院2015年学术报告第45期
【主 题】Semiparametric Methods for Dimension Reduction under Linearity Conditions
【报告人】林巍 副教授
Ohio University
【时 间】 2015年10月08日（星期四）16:00－17:00
【地 点】 上海财经大学统计与管理学院大楼1208室
【语 言】 英文
【摘 要】Dimension reduction has been one of the most popular topics in regression analysis in the past two decades. It sees much progress with the introduction of the sliced inverse regression (SIR, Li 1991) technique and since then many inference methods have been proposed in the literature. While there are nonparametric alternatives (Xia 2002), vast majority of these methods center around the idea of inverse regression and assume the so-called linearity condition. In the past few years, however, semiparametric methods have brought much development into the field (Ma and Zhu, 2012) without assuming the linearity condition. In this talk, we will introduce how semiparametric methods work in the field of dimension reduction, and show how the linearity condition affects the corresponding results.
