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A unified empirical likelihood approach for testing MCAR and subsequent estimation

dc.contributor.authorZhang, Shixiao
dc.contributor.authorHan, Peisong
dc.contributor.authorWu, Changbao
dc.date.accessioned2019-02-12T20:25:17Z
dc.date.available2020-05-01T18:03:25Zen
dc.date.issued2019-03
dc.identifier.citationZhang, Shixiao; Han, Peisong; Wu, Changbao (2019). "A unified empirical likelihood approach for testing MCAR and subsequent estimation." Scandinavian Journal of Statistics 46(1): 272-288.
dc.identifier.issn0303-6898
dc.identifier.issn1467-9469
dc.identifier.urihttps://hdl.handle.net/2027.42/147870
dc.description.abstractFor 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.
dc.publisherJohn Wiley & Sons, Inc.
dc.subject.othermissingness mechanism
dc.subject.othermissing completely at random
dc.subject.otherempirical likelihood
dc.subject.othercalibration
dc.titleA unified empirical likelihood approach for testing MCAR and subsequent estimation
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelStatistics (Mathematical)
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/147870/1/sjos12351_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/147870/2/sjos12351.pdf
dc.identifier.doi10.1111/sjos.12351
dc.identifier.sourceScandinavian Journal of Statistics
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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