Empirical likelihood for response differences in two linear regression models with missing data

Consider two linear models X i = U ′ i β + ε i Y j = V ′ j γ + η j with response variables missing at random. In this paper, we assume that X , Y are missing at random (MAR) and use the inverse probability weighted imputation to produce ‘complete’ data sets for X and Y. Based on these data sets, we...

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Bibliographic Details
Published in:Acta Mathematicae Applicatae Sinica 2015-10, Vol.31 (4), p.963-976
Main Authors: Qin, Yong-song, Qiu, Tao, Lei, Qing-zhu
Format: Article
Language:English
Subjects:
Applications of Mathematics
Math Applications in Computer Science
Mathematical and Computational Physics
Mathematics
Mathematics and Statistics
Theoretical
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Summary:Consider two linear models X i = U ′ i β + ε i Y j = V ′ j γ + η j with response variables missing at random. In this paper, we assume that X , Y are missing at random (MAR) and use the inverse probability weighted imputation to produce ‘complete’ data sets for X and Y. Based on these data sets, we construct an empirical likelihood (EL) statistic for the difference of X and Y (denoted as Δ), and show that the EL statistic has the limiting distribution of χ 1 2 , which is used to construct a confidence interval for Δ. Results of a simulation study on the finite sample performance of EL-based confidence intervals on Δ are reported.
ISSN:0168-9673
1618-3932
DOI:10.1007/s10255-015-0516-y