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Quantile regression with doubly censored data

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  • Lin, Guixian
  • He, Xuming
  • Portnoy, Stephen

Abstract

Quantile regression offers a semiparametric approach to modeling data with possible heterogeneity. It is particularly attractive for censored responses, where the conditional mean functions are unidentifiable without parametric assumptions on the distributions. A new algorithm is proposed to estimate the regression quantile process when the response variable is subject to double censoring. The algorithm distributes the probability mass of each censored point to its left or right appropriately, and iterates towards self-consistent solutions. Numerical results on simulated data and an unemployment duration study are given to demonstrate the merits of the proposed method.

Suggested Citation

  • Lin, Guixian & He, Xuming & Portnoy, Stephen, 2012. "Quantile regression with doubly censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 797-812.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:4:p:797-812
    DOI: 10.1016/j.csda.2011.03.009
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    References listed on IDEAS

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    8. Portnoy, Stephen, 2014. "The jackknife’s edge: Inference for censored regression quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 273-281.
    9. ChunJing Li & Yun Li & Xue Ding & XiaoGang Dong, 2020. "DGQR estimation for interval censored quantile regression with varying-coefficient models," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-17, November.
    10. Bilias, Yannis & Florios, Kostas & Skouras, Spyros, 2019. "Exact computation of Censored Least Absolute Deviations estimator," Journal of Econometrics, Elsevier, vol. 212(2), pages 584-606.
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