TITLE: Variable selection using dimension reduction model

SPEAKER: Dr. Wenxuan Zhong

ABSTRACT:

In this talk, a forward screening selection procedure will be discussed
under the sufficient dimension reduction framework, in which the response
variable is influenced by a subset of predictors through an unknown
function of a few linear combinations of them. Unlike linear model, our
proposed method does not impose a special form of relationship (such as
linear) between the response variable and the predictor variables. Our
method selects variables that attain the maximum correlation between the
transformed response and the linear combination of the variables. Various
asymptotic properties, and in particular, its variable selection
performance under diverging number of predictors and sample size has been
investigated and will be discussed in this talk. The empirical performance
of the procedure will be demonstrated in functional genomic analysis.

Contact: Wenxuan Zhong <wenxuan@illinois.edu>