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A transformation approach in linear mixed-effects models with informative missing responses

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  • J. Shao
  • J. Zhang

Abstract

We consider a linear mixed-effects model in which the response panel vector has missing components and the missing data mechanism depends on observed data as well as missing responses through unobserved random effects. Using a transformation of the data that eliminates the random effects, we derive asymptotically unbiased and normally distributed estimators of certain model parameters. Estimators of model parameters that cannot be estimated using the transformed data are also constructed, and their asymptotic unbiasedness and normality are established. Simulation results are presented to examine the finite sample performance of the proposed estimators and a real data example is discussed.

Suggested Citation

  • J. Shao & J. Zhang, 2015. "A transformation approach in linear mixed-effects models with informative missing responses," Biometrika, Biometrika Trust, vol. 102(1), pages 107-119.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:1:p:107-119.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu069
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    Cited by:

    1. Lyu Ni & Jun Shao, 2023. "Estimation with multivariate outcomes having nonignorable item nonresponse," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(1), pages 1-15, February.
    2. Yongge Tian, 2017. "Transformation approaches of linear random-effects models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 583-608, November.

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