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arXiv · 1403.3457

Valid post-correction inference for censored regression problems

Yuekai Sun·Jonathan E. Taylor
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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 /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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Yuekai Sun, Jonathan E. Taylor. 2014-03-13. Valid post-correction inference for censored regression problems. https://arxiv.org/abs/1403.3457

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