Abstract Details
Activity Number:
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80
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Type:
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Contributed
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Date/Time:
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Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #311134
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View Presentation
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Title:
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Exact Inference for Linear and Logistic Regression After Model Selection
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Author(s):
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Jason Lee*+ and Jonathan Taylor and Yuekai Sun and Dennis Sun
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Companies:
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Stanford University and Stanford University and Stanford University and Stanford University
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Keywords:
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Post model selection inference ;
confidence intervals ;
lasso ;
marginal screening ;
correlation screening
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Abstract:
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We develop a framework for inference in Linear regression and Logistic regression after model selection using marginal screening or Lasso. At the core of this framework is a result that characterizes the exact (non-asymptotic) distribution of a pivot.
This pivot allows us to (i) construct valid confidence intervals for the selected coefficients that account for the selection procedure, and (ii) devise a test statistic that has an exact (non-asymptotic) $\unif(0,1)$ distribution under the null hypothesis that all relevant variables have been included in the model.
We will also discuss extensions of the methodology to other GLM regression.
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Authors who are presenting talks have a * after their name.
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