Abstract Details
Activity Number:
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170
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract #313639
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View Presentation
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Title:
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Valid Post-Correction Inference for Censored Regression Problems
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Author(s):
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Yuekai Sun*+ and Jonathan Taylor
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Companies:
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Stanford University and Stanford University
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Keywords:
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censored regression ;
Tobit model ;
two-step estimator ;
post-correction inference
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Abstract:
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Two-step estimators often called upon to fit censored regression models in many areas of science and engineering. Since censoring incurs a bias in the naive least-squares fit, a two-step estimator first estimates the bias and then fits a corrected linear model. We develop a framework for performing valid \emph{post-correction inference} with two-step estimators. By exploiting recent results on post-selection inference, we obtain valid confidence intervals and significance tests for the fitted coefficients.
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Authors who are presenting talks have a * after their name.
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