Special Lecture Series:
Dimension Reduction in Regression
by
Visiting Prof. Lixing Zhu
Chair Professor and Department Head
Department of Mathematics
Hong Kong Baptist University
This lecture series consists of three lectures to introduce high-dimensional semiparametric regression estimation procedures as follows
October 6, 2014 – Lecture 3
Reception to follow, 5:30 pm-6:30 pm in Noether Lounge, AUST 326
Estimation: can inverse regression methods solve forward regression problems?
In this lecture, mainly two more methods are introduced to identify and estimate the central subspace. The so-called inverse regression notion is introduced for this purpose. The method is based on the conditional expectation of the covariate given the response rather than the regression function that is the conditional expectation of the response given the covariate. This is why we call it the inverse regression method. The methods are called the sliced inverse regression (SIR) and sliced average variance estimator (SAVE). In this lecture, some further developments will also be described very briefly in the sufficient dimension reduction field.
TIME AND PLACE FOR ALL LECTURES
4:40 – 5:30 p.m.
Philip E. Austin Building – Room 344
Coffee will be served at 4:10 in room 326
For more information, contact: Tracy Burke at tracy.burke@uconn.edu