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Activity Number: 608
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318884 View Presentation
Title: Group Feature Selection in Ultrahigh-Dimensional Generalized Varying-Coefficient Linear Models
Author(s): Songshan Yang* and Runze Li
Companies: and Penn State University
Keywords: Generalized linear models ; varying-coefficient models ; penalized likelihood ; ultrahigh dimensional data

The varying-coefficient model is an important nonparametric statistical model that allow us to examine how the effects of covariates vary with exposure variables. When the number of covariates can be huge and much larger than the sample size, the issue of variable selection arrives. In this work, the proposed procedure is distinguished from the existing sure independence screening (SIS) procedures (Fan and Song, 2010, Fan, Ma and Dai, 2013, Liu, Li and Wu, 2014) in that the proposed procedure is based on joint likelihood of potential active predictors, and therefore is not a marginal screening procedure. The proposed procedure can effectively identify active predictors that are jointly dependent but marginal independent of the response without performing an iterative procedure. We develop a computationally effective algorithm to carry out the proposed procedure and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure posses the sure screening property. That is, with probability tending to one, the selected variable set includes the actual active predictors.

Authors who are presenting talks have a * after their name.

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