Nonlinear dependence metrics with applications to variable selection and model checking


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. 


June 13th, 2017

15:00 ~ 16:30


Yaowu Zhang, Shanghai University of Finance and Economics

Yaowu Zhang is a PhD student in the School of Statistics and Management in Shanghai University of Finance and Economics (advised by Prof. Liping Zhu). He got his bachelor’s degree of engineering from Southeast University in 2012. His research interest mainly include non-parametric and semi-parametric modeling, high-dimensional statistical inference, sufficient dimension reduction, variable selection and model checking.


Room 102, School of Information Management & Engineering, Shanghai University of Finance & Economics