Statistics Seminar - Variable selection using dimension reduction model

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TITLE: Variable selection using dimension reduction model

SPEAKER: Dr. Wenxuan Zhong


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>


  • Workflow Status:Published
  • Created By:Anita Race
  • Created:02/17/2012
  • Modified By:Fletcher Moore
  • Modified:10/07/2016


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