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An efficient empirical likelihood approach for estimating equations with missing data

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  • Cheng Yong Tang
  • Yongsong Qin

Abstract

We explore the use of estimating equations for efficient statistical inference in case of missing data. We propose a semiparametric efficient empirical likelihood approach, and show that the empirical likelihood ratio statistic and its profile counterpart asymptotically follow central chi-square distributions when evaluated at the true parameter. The theoretical properties and practical performance of our approach are demonstrated through numerical simulations and data analysis. Copyright 2012, Oxford University Press.

Suggested Citation

  • Cheng Yong Tang & Yongsong Qin, 2012. "An efficient empirical likelihood approach for estimating equations with missing data," Biometrika, Biometrika Trust, vol. 99(4), pages 1001-1007.
    • Handle: RePEc:oup:biomet:v:99:y:2012:i:4:p:1001-1007
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    File URL: http://hdl.handle.net/10.1093/biomet/ass045
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    Cited by:

    1. Cui, Li-E & Zhao, Puying & Tang, Niansheng, 2022. "Generalized empirical likelihood for nonsmooth estimating equations with missing data," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    2. Zhong, Ping-Shou & Chen, Sixia, 2014. "Jackknife empirical likelihood inference with regression imputation and survey data," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 193-205.
    3. Zhong Guan & Jing Qin, 2017. "Empirical likelihood method for non-ignorable missing data problems," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 113-135, January.
    4. Jin Qiu & Lang Wu, 2015. "A moving blocks empirical likelihood method for longitudinal data," Biometrics, The International Biometric Society, vol. 71(3), pages 616-624, September.
    5. Hong-Xia Xu & Guo-Liang Fan & Han-Ying Liang, 2017. "Hypothesis test on response mean with inequality constraints under data missing when covariables are present," Statistical Papers, Springer, vol. 58(1), pages 53-75, March.
    6. Shuanghua Luo & Changlin Mei & Cheng-yi Zhang, 2017. "Smoothed empirical likelihood for quantile regression models with response data missing at random," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(1), pages 95-116, January.
    7. Xuerong Chen & Alan T. K. Wan & Yong Zhou, 2015. "Efficient Quantile Regression Analysis With Missing Observations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 723-741, June.
    8. Dong, Yuexiao & Xia, Qi & Tang, Cheng Yong & Li, Zeda, 2018. "On sufficient dimension reduction with missing responses through estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 67-77.

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