Li-Ping Zhu

East China Normal University

Sufficient Dimension Reduction for High Dimensional Data in Regression
In this talk, we propose a model-free independence screening procedure to select the subset of active predictors by using the diagonal elements of an average partial mean estimation matrix.

Dimension reduction in ultrahigh dimensional feature space characterizes various contemporary problems in scientific discoveries. In this talk, we propose a model-free independence screening procedure to select the subset of active predictors by using the diagonal elements of an average partial mean estimation matrix. The new proposal possesses the sure independence screening property for a wide range of semi-parametric regressions, i.e. it guarantees to select the subset of active predictors with probability approaching to one as the sample size diverges.

Thursday, January 14, 2010 - 3:30pm
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