The weighted least square based estimators with censoring indicators missing at random |
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Authors: | Xiayan Li Qihua Wang |
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Institution: | 1. Department of Statistics and Finance, University of Science and Technology of China, Hefei, China;2. Academy of Mathematics and Systems, Chinese Academy of Science, Beijing 100190, China |
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Abstract: | In this paper, we study linear regression analysis when some of the censoring indicators are missing at random. We define regression calibration estimate, imputation estimate and inverse probability weighted estimate for the regression coefficient vector based on the weighted least squared approach due to Stute (1993), and prove all the estimators are asymptotically normal. A simulation study was conducted to evaluate the finite properties of the proposed estimators, and a real data example is provided to illustrate our methods. |
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Keywords: | Censoring indicator Weighted least square Imputation Regression calibration Augmented inverse probability weighting Missing at random |
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