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An Efficient Multiple Imputation Approach for Estimating Equations with Response Missing at Random and High-Dimensional Covariates

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Abstract

Empirical-likelihood-based inference for parameters defined by the general estimating equations of Qin and Lawless (1994) remains an active research topic. When the response is missing at random (MAR) and the dimension of covariate is not low, the authors propose a two-stage estimation procedure by using the dimension-reduced kernel estimators in conjunction with an unbiased estimating function based on augmented inverse probability weighting and multiple imputation (AIPW-MI) methods. The authors show that the resulting estimator achieves consistency and asymptotic normality. In addition, the corresponding empirical likelihood ratio statistics asymptotically follow central chi-square distributions when evaluated at the true parameter. The finite-sample performance of the proposed estimator is studied through simulation, and an application to HIV-CD4 data set is also presented.

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Authors and Affiliations

  1. School of Statistics and Data Science, LPMC & KLMDASR, Nankai University, Tianjin, 300071, China

    Lei Wang, Siying Sun & Zheng Xia

Authors
  1. Lei Wang
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  2. Siying Sun
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  3. Zheng Xia
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Corresponding author

Correspondence to Lei Wang.

Additional information

This paper was supported by the National Natural Science Foundation of China under Grant Nos. 11871287, 11501208, 11771144, 11801359, the Natural Science Foundation of Tianjin under Grant No. 18JCYBJC41100, Fundamental Research Funds for the Central Universities and the Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin. The first two authors contributed equally to this work.

This paper was recommended for publication by Editor SHAO Jun.

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Wang, L., Sun, S. & Xia, Z. An Efficient Multiple Imputation Approach for Estimating Equations with Response Missing at Random and High-Dimensional Covariates. J Syst Sci Complex 34, 440–464 (2021). https://doi.org/10.1007/s11424-020-9133-9

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  • DOI: https://doi.org/10.1007/s11424-020-9133-9

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