JSM 2004 - Toronto

Abstract #301836

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Activity Number: 276
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #301836
Title: A New Approach to Data Dimension Reduction and Variable Selection
Author(s): Shiying Wu*+ and Jun Liu
Companies: RTI International and RTI International
Address: 3040 Cornwallis Rd., RTP, NC, 27709,
Keywords: principal components ; statistical testing
Abstract:

Variable selection and data dimension reduction is a commonly encounter problem in many scientific disciplines. When analyzing the full scan chromatograms from breath samples of asthmatic children, we applied an innovative approach to identify dozens VOCs, among hundreds of potential targets, that might be triggers of asthma attack. In our new approach, the independent variable was regressed on the rest of the principle components after the least important principal components were first being removed. The regression equation in principal components was translated back to the equation in the original variables. The approach takes the advantage of PCA and provides results that are directly interpretable. We attempt to provide a formal statistical inference framework within which formal statistical testing can be performed for variable selection. We will also attempt to address the multiple test problem encountered in this type of problems.

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