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An improvement on the efficiency of complete-case-analysis with nonignorable missing covariate data

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  • Jing Sun

    (Ludong University)

Abstract

This paper develops a weighted composite quantile regression method for linear models where some covariates are missing not at random but the missingness is conditionally independent of the response variable. It is known that complete case analysis (CCA) is valid under these missingness assumptions. By fully utilizing the information from incomplete data, empirical likelihood-based weights are obtained to conduct the weighted composite quantile regression. Theoretical results show that the proposed estimator is more efficient than the CCA one if the probability of missingness on the fully observed variables is correctly specified. Besides, the proposed algorithm is computationally simple and easy to implement. The methodology is illustrated on simulated data and a real data set.

Suggested Citation

  • Jing Sun, 2020. "An improvement on the efficiency of complete-case-analysis with nonignorable missing covariate data," Computational Statistics, Springer, vol. 35(4), pages 1621-1636, December.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:4:d:10.1007_s00180-020-00964-6
    DOI: 10.1007/s00180-020-00964-6
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    References listed on IDEAS

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    Cited by:

    1. Xiaohui Yuan & Yong Li & Xiaogang Dong & Tianqing Liu, 2022. "Optimal subsampling for composite quantile regression in big data," Statistical Papers, Springer, vol. 63(5), pages 1649-1676, October.

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