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A weighted quantile regression for randomly truncated data

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  • Zhou, Weihua

Abstract

Quantile regression offers great flexibility in assessing covariate effects on the response. In this article, based on the weights proposed by He and Yang (2003), we develop a new quantile regression approach for left truncated data. Our method leads to a simple algorithm that can be conveniently implemented with R software. It is shown that the proposed estimator is strongly consistent and asymptotically normal under appropriate conditions. We evaluate the finite sample performance of the proposed estimators through extensive simulation studies.

Suggested Citation

  • Zhou, Weihua, 2011. "A weighted quantile regression for randomly truncated data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 554-566, January.
  • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:554-566
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    References listed on IDEAS

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    1. Lai, Tze Leung & Ying, Zhiliang, 1992. "Linear rank statistics in regression analysis with censored or truncated data," Journal of Multivariate Analysis, Elsevier, vol. 40(1), pages 13-45, January.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, May.
    4. Peng, Limin & Huang, Yijian, 2008. "Survival Analysis With Quantile Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 637-649, June.
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    Cited by:

    1. repec:spr:aistmt:v:70:y:2018:i:1:d:10.1007_s10463-016-0587-4 is not listed on IDEAS
    2. repec:eee:csdana:v:113:y:2017:i:c:p:53-63 is not listed on IDEAS
    3. Jung-Yu Cheng & Shinn-Jia Tzeng, 2014. "Quantile regression of right-censored length-biased data using the Buckley–James-type method," Computational Statistics, Springer, vol. 29(6), pages 1571-1592, December.

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