Nonlinear dependence metrics with applications to variable selection and model checking

讲座简介

It is of fundamental importance to infer how the conditional mean of the response varies with the predictors. Sufficient dimension reduction techniques reduce the dimension by identifying a minimal set of linear combinations of the original predictors without loss of information.

In this talk, we first propose a semiparametric approach to reduce the covariate dimension. The method bypasses the conventional inverse regression procedure hence seamlessly avoids the potential difficulties related to the dimension of the response. In addition, coupled with a proper parameterization, the approach allows for statistical inference of the dimension reduction subspace for a wide range of models. 

时间

2017-06-13

15:00 ~ 16:30

主讲人

张耀武, 上海财经大学

地点

信息管理与工程学院102室
上海财经大学（第三教学楼西侧）
上海市杨浦区武东路100号