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Abstract Details

Activity Number: 568
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #305501
Title: Sequential Sufficient Dimension Reduction for Large P, Small N Problems
Author(s): Haileab Hilafu*+ and Xiangrong Yin
Companies: The University of Georgia and University of Georgia
Address: 101 Cedar Street, Athens, GA, 30602, United States
Keywords: Central subspace ; High-dimensional data ; Large p small n ; Sufficient dimension reduction ; Sufficient variable selection ; Feature Screening

In this talk, I will introduce a new but very simple framework to tackle the large p small n problems. The framework decomposes the data into pieces so that existing methods can be applied. We propose two separate paths to implement the framework. Our paths provide sufficient procedures for identifying informative variables, sequentially. The paths are very general and shall have a great impact in high-dimensional data analysis. We shall illustrate their efficacy via simulation and a real data application by using sufficient dimension reduction and sufficient variable selection methods.

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