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Activity Number: 80
Type: Contributed
Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #312521 View Presentation
Title: Variable Screening Under Dependence
Author(s): Teng Zhang*+ and Jessie Jeng
Companies: North Carolina State University and North Carolina State University
Keywords: Variable screening ; False negative control ; Dependence ; Data-driven ; Dimension reduction
Abstract:

In ultra high dimensional study, variable screening is widely used in variable selection and dimension reduction. Practitioners in real applications often use subjective criterion to select a prefixed number of top-ranked variables to reduce the dimension of the data. However, how to efficiently determine the prefixed number remains an open question. In this paper, we provide a consistent estimator for the proportion of important variables under dependence. Based on the estimated proportion, a data-driven screening procedure is proposed to efficiently control the false negative at a desirable level. The proposed method is applied to several examples in genomic data analysis.


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