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Activity Number: 170
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #313639 View Presentation
Title: Valid Post-Correction Inference for Censored Regression Problems
Author(s): Yuekai Sun*+ and Jonathan Taylor
Companies: Stanford University and Stanford University
Keywords: censored regression ; Tobit model ; two-step estimator ; post-correction inference
Abstract:

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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