Abstract Details
Activity Number:
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593
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #307619 |
Title:
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Identifying Multiple Regulation in Semiparametric Regression Models
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Author(s):
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Denis Agniel*+ and Tianxi Cai and Katherine P. Liao and Robert M. Plenge
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Companies:
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and Harvard University and Brigham and Women's Hospital and Brigham and Women's Hospital
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Keywords:
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semi-parametric regression ;
sparse regression ;
resampling ;
closed testing ;
multiple regulation ;
hierarchical lasso
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Abstract:
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Often it is suspected that many related outcomes have a shared, sparse set of predictors. In genetics, researchers might hypothesize that a group of related diseases share a common genetic basis. In these cases, from a large set of potential predictors, we seek to identify a small set related with a set of outcomes. Furthermore, since not every predictor that is related to the outcomes will necessarily be related to all of them, we would like to identify for each predictor which outcomes it is associated with. In particular, we want to identify predictors that are important for multiple outcomes, which we will call "multiple regulators". This type of problem has been well studied in the case of multivariate linear regression, but we propose a method for identifying multiple regulation in semi-parametric regression models based on the hierarchical lasso (Zhou and Zhu, 2010). We further offer a resampling method to assess the variability in our estimator. And we finally propose a closed testing procedure to assess multiple regulation in the presence of randomness in the observed data.
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Authors who are presenting talks have a * after their name.
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