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Weighted quantile regression for longitudinal data using empirical likelihood

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Abstract

This paper proposes a new weighted quantile regression model for longitudinal data with weights chosen by empirical likelihood (EL). This approach efficiently incorporates the information from the conditional quantile restrictions to account for within-subject correlations. The resulted estimate is computationally simple and has good performance under modest or high within-subject correlation. The efficiency gain is quantified theoretically and illustrated via simulation and a real data application.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 11401048, 11301037, 11571051 and 11201174) and the Natural Science Foundation for Young Scientists of Jilin Province of China (Grant Nos. 20150520055JH and 20150520054JH). The authors thank anonymous referees for their careful reading of the paper and insightful comments.

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Authors and Affiliations

  1. School of Basic Science, Changchun University of Technology, Changchun, 130012, China

    XiaoHui Yuan & XiaoGang Dong

  2. Department of Mathematics, Washington University in St. Louis, St. Louis, 63130, USA

    Nan Lin

  3. School of Mathematics, Jilin University, Changchun, 130012, China

    TianQing Liu

Authors
  1. XiaoHui Yuan
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  2. Nan Lin
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  3. XiaoGang Dong
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  4. TianQing Liu
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Correspondence to TianQing Liu.

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Yuan, X., Lin, N., Dong, X. et al. Weighted quantile regression for longitudinal data using empirical likelihood. Sci. China Math. 60, 147–164 (2017). https://doi.org/10.1007/s11425-015-0175-y

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