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Abstract Details
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Activity Number:
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346
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #300961 |
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Title:
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Variable Selection Methods and Applications
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Author(s):
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June Luo*+
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Companies:
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Clemson University
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Address:
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229 Barre Hall, Clemson, SC, 29631,
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Keywords:
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high dimension ;
robust ;
false discovery rate
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Abstract:
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Statisticians have recently proposed some methods for detecting the genes with outlier expressions-a feature that has been found in biological datasets. The major attraction of these methods is their ability to select the variables which show systematic decrease or increase in only a subset of samples in the disease group. In this article, I propose an alternative method to rank the variables with systematic increase or decrease. The proposed statistic is very simple to implement. Simulations show that the proposed statistic has a more powerful ability to pick the variables than some methods in literature. All the discussed statistics are applied to the gross domestic product (GDP) growth rate data for 44 countries. The GDP data ranges from year 2006 to year 2008. The result demonstrates that the new method is efficient in detecting the variables with systematic decrease.
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