A unified empirical likelihood approach for testing MCAR and subsequent estimation

For an estimation with missing data, a crucial step is to determine if the data are missing completely at random (MCAR), in which case a complete‐case analysis would suffice. Most existing tests for MCAR do not provide a method for a subsequent estimation once the MCAR is rejected. In the setting of...

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Bibliographic Details
Published inScandinavian journal of statistics Vol. 46; no. 1; pp. 272 - 288
Main Authors Zhang, Shixiao, Han, Peisong, Wu, Changbao
Format Journal Article
LanguageEnglish
Published Oxford Blackwell Publishing Ltd 01.03.2019
Subjects
calibration
Empirical analysis
empirical likelihood
missing completely at random
Missing data
missingness mechanism
Regression analysis
Regression models
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Summary:For an estimation with missing data, a crucial step is to determine if the data are missing completely at random (MCAR), in which case a complete‐case analysis would suffice. Most existing tests for MCAR do not provide a method for a subsequent estimation once the MCAR is rejected. In the setting of estimating means, we propose a unified approach for testing MCAR and the subsequent estimation. Upon rejecting MCAR, the same set of weights used for testing can then be used for estimation. The resulting estimators are consistent if the missingness of each response variable depends only on a set of fully observed auxiliary variables and the true outcome regression model is among the user‐specified functions for deriving the weights. The proposed method is based on the calibration idea from survey sampling literature and the empirical likelihood theory.
ISSN:0303-6898
1467-9469
DOI:10.1111/sjos.12351