This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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295
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #308203 |
Title:
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Stepwise Paring Down Variation for Identifying Influential Multifactor Interactions
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Author(s):
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Jing-Shiang Hwang*+
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Companies:
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Academia Sinica
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Address:
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128 Academia Road, Section 2, Taipei, , Taiwan
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Keywords:
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Variable selection ;
High dimensional data ;
Complex disease ;
QTL ;
ANOVA
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Abstract:
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We consider the problem of identifying influential sets of factors related to a continuous response variable from a large number of factor variables. The ability of most available methods to reveal more true factors and fewer false ones often relies heavily on tuning parameters which is still a difficult task. This article provides a completely different solution with a simple and novel idea for the identification of influential variables. The method is simple as it involves only repeatedly implementing single-term analysis of variation. The main idea is to stepwise pare down the total variation of responses so that the remaining influential sets of factors have increased chances of being identified. We have developed an R package and demonstrated its competitive performance compared to the popular group lasso, logic regression and Bayesian QTL mapping methods in the literature.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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