In this talk, I will present the Correlation Pursuit (COP) method, a variable selection procedure developed under the sufficient dimension reduction framework. Unlike the conventional stepwise, COP does not impose a special form of relationship between the response variable and the predictor variables. The COP procedure selects variables that maximize the correlation between the transformed response and linear combinations of the predictors. Various asymptotic properties of COP procedure will be discussed, and in particular, its variable selection performance under diverging number of predictors and sample size will be discussed.
<a href="http://www.stat.uiuc.edu/people/faculty/zhongw.shtml">University of Illinois at Urbana-Champaign</a>
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