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An empirical likelihood approach to quantile regression with auxiliary information

Author

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  • Tang, Cheng Yong
  • Leng, Chenlei

Abstract

We consider how to incorporate auxiliary information to improve quantile regression via empirical likelihood. We propose a novel framework and show that our approach yields more efficient estimates compared to those from the conventional quantile regression. The efficiency gain is quantified theoretically and demonstrated empirically via simulation studies.

Suggested Citation

  • Tang, Cheng Yong & Leng, Chenlei, 2012. "An empirical likelihood approach to quantile regression with auxiliary information," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 29-36.
  • Handle: RePEc:eee:stapro:v:82:y:2012:i:1:p:29-36
    DOI: 10.1016/j.spl.2011.09.003
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Qin, Gengsheng & Tsao, Min, 2003. "Empirical likelihood inference for median regression models for censored survival data," Journal of Multivariate Analysis, Elsevier, vol. 85(2), pages 416-430, May.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Whang, Yoon-Jae, 2006. "Smoothed Empirical Likelihood Methods For Quantile Regression Models," Econometric Theory, Cambridge University Press, vol. 22(02), pages 173-205, April.
    5. Sanjay Chaudhuri & Mark S. Handcock & Michael S. Rendall, 2008. "Generalized linear models incorporating population level information: an empirical-likelihood-based approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 311-328.
    6. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, April.
    7. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
    8. Mu, Yunming & He, Xuming, 2007. "Power Transformation Toward a Linear Regression Quantile," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 269-279, March.
    9. Song Xi Chen & Denis H. Y. Leung & Jing Qin, 2008. "Improving semiparametric estimation by using surrogate data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 803-823.
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    Cited by:

    1. repec:spr:stpapr:v:59:y:2018:i:2:d:10.1007_s00362-016-0784-5 is not listed on IDEAS
    2. Peixin Zhao & Xinrong Tang, 2016. "Imputation based statistical inference for partially linear quantile regression models with missing responses," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(8), pages 991-1009, November.

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