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Quantile regression with doubly censored data

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

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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 4 ()
    Pages: 797-812

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    Handle: RePEc:eee:csdana:v:56:y:2012:i:4:p:797-812

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Accelerated failure time model; Kaplan–Meier; Survival analysis; Self-consistent; Semiparametric; Random censoring;

    References

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    1. Laura Wichert & Ralf A. Wilke, 2008. "Simple non-parametric estimators for unemployment duration analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(1), pages 117-126.
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    8. Khan, Shakeeb & Tamer, Elie, 2009. "Inference on endogenously censored regression models using conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 152(2), pages 104-119, October.
    9. Debruyne, M. & Hubert, M. & Portnoy, S. & Vanden Branden, K., 2008. "Censored depth quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1604-1614, January.
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    13. Hong H. & Chernozhukov V., 2002. "Three-Step Censored Quantile Regression and Extramarital Affairs," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 872-882, September.
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    15. Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
    16. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    17. Stephen R. Cosslett, 2004. "Efficient Semiparametric Estimation of Censored and Truncated Regressions via a Smoothed Self-Consistency Equation," Econometrica, Econometric Society, vol. 72(4), pages 1277-1293, 07.
    18. Wang, Huixia Judy & Wang, Lan, 2009. "Locally Weighted Censored Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1117-1128.
    19. Khan, Shakeeb & Tamer, Elie, 2007. "Partial rank estimation of duration models with general forms of censoring," Journal of Econometrics, Elsevier, vol. 136(1), pages 251-280, January.
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    Cited by:
    1. Portnoy, Stephen, 2014. "The jackknife’s edge: Inference for censored regression quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 273-281.
    2. Jakob W. Messner & Achim Zeileis & Jochen Broecker & Georg J. Mayr, 2013. "Improved Probabilistic Wind Power Forecasts with an Inverse Power Curve Transformation and Censored Regression," Working Papers 2013-01, Faculty of Economics and Statistics, University of Innsbruck.
    3. Polpo, A. & de Campos, C.P. & Sinha, D. & Lipsitz, S. & Lin, J., 2014. "Transform both sides model: A parametric approach," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 903-913.

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