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Rank-Based Empirical Likelihood for Regression Models with Responses Missing at Random

  • Huybrechts F. Bindele
  • Yichuan Zhao
Chapter
Part of the ICSA Book Series in Statistics book series (ICSABSS)

Abstract

In this paper, a general regression model with responses missing at random is considered. From an imputed rank-based objective function, a rank-based estimator is derived and its asymptotic distribution is established under mild conditions. Inference based on the normal approximation approach results in under coverage or over coverage issues. In order to address these issues, we propose an empirical likelihood approach based on the rank-based objective function, from which its asymptotic distribution is established. Extensive Monte Carlo simulation experiments under different settings of error distributions with different response probabilities are considered. The simulation results show that the proposed approach has better performance for the regression parameters compared to the normal approximation approach and its least-squares counterpart. Finally, a data example is provided to illustrate our method.

Notes

Acknowledgements

The authors would like to thank the two reviewers for their helpful comments. The research of Yichuan Zhao is supported by the National Security Agency grant.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsUniversity of South AlabamaMobileUSA
  2. 2.Department of Mathematics and StatisticsGeorgia State UniversityAtlantaUSA

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