The weighted least square based estimators with censoring indicators missing at random

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...

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Bibliographic Details
Published inJournal of statistical planning and inference Vol. 142; no. 11; pp. 2913 - 2925
Main Authors Li, Xiayan, Wang, Qihua
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.11.2012
Subjects
Augmented inverse probability weighting
Censoring indicator
Imputation
Missing at random
Regression calibration
Weighted least square
Censoring indicator
Augmented inverse probability weighting
Imputation
Regression calibration
Missing at random
Weighted least square
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Summary: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.
ISSN:0378-3758
1873-1171
DOI:10.1016/j.jspi.2012.04.016