Abstract Details
Activity Number:
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455
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract #312304
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View Presentation
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Title:
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Hypothesis Testing for High-Dimensional Linear Regression with Linear Constraints
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Author(s):
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Pixu Shi*+ and Hongzhe Li
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Companies:
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University of Pennsylvania and University of Pennsylvania
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Keywords:
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Compositional data ;
High-dimensional regression ;
Linear constraints ;
Log-contrast model ;
Microbiome ;
Scaled Lasso
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Abstract:
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Motivated by research problems stemming from the analysis of compositional microbiome data, we propose a general method for constructing confidence intervals and p-values for high-dimensional linear models with linear constraints. Our method is based on the construction of a de-biased version of the l1 regularized constrained estimator. Theoretical justifications are provided. The performance of our method is evaluated using simulations on high-dimensional log-contrast model, a model designed for compositional data analysis and a special case of high-dimensional linear model with linear constraints. We also apply our method to a microbiome study that relates body mass index to human gut microbiome composition. Three genera of bacteria are identified to be statistically significant at level of 0.05.
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Authors who are presenting talks have a * after their name.
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