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An informative subset-based estimator for censored quantile regression

Author

Listed:
  • Yanlin Tang
  • Huixia Wang
  • Xuming He
  • Zhongyi Zhu

    ()

Abstract

Quantile regression in the presence of fixed censoring has been studied extensively in the literature. However, existing methods either suffer from computational instability or require complex procedures involving trimming and smoothing, which complicates the asymptotic theory of the resulting estimators. In this paper, we propose a simple estimator that is obtained by applying standard quantile regression to observations in an informative subset. The proposed method is computationally convenient and conceptually transparent. We demonstrate that the proposed estimator achieves the same asymptotical efficiency as the Powell’s estimator, as long as the conditional censoring probability can be estimated consistently at a nonparametric rate and the estimated function satisfies some smoothness conditions. A simulation study suggests that the proposed estimator has stable and competitive performance relative to more elaborate competitors. Copyright Sociedad de Estadística e Investigación Operativa 2012

Suggested Citation

  • Yanlin Tang & Huixia Wang & Xuming He & Zhongyi Zhu, 2012. "An informative subset-based estimator for censored quantile regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(4), pages 635-655, December.
  • Handle: RePEc:spr:testjl:v:21:y:2012:i:4:p:635-655
    DOI: 10.1007/s11749-011-0266-y
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    File URL: http://hdl.handle.net/10.1007/s11749-011-0266-y
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    References listed on IDEAS

    as
    1. 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.
    2. Xiaohong Chen & Oliver Linton & Ingrid Van Keilegom, 2003. "Estimation of Semiparametric Models when the Criterion Function Is Not Smooth," Econometrica, Econometric Society, vol. 71(5), pages 1591-1608, September.
    3. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
    4. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, March.
    5. Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
    6. Koenker, Roger, 2008. "Censored Quantile Regression Redux," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i06).
    7. Khan, Shakeeb & Powell, James L., 2001. "Two-step estimation of semiparametric censored regression models," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 73-110, July.
    8. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    9. repec:taf:gnstxx:v:22:y:2010:i:4:p:443-457 is not listed on IDEAS
    10. Koenker, Roger & Park, Beum J., 1996. "An interior point algorithm for nonlinear quantile regression," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 265-283.
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    Cited by:

    1. repec:bla:istatr:v:84:y:2016:i:3:p:327-344 is not listed on IDEAS
    2. Feng, Xiang-Nan & Wang, Yifan & Lu, Bin & Song, Xin-Yuan, 2017. "Bayesian regularized quantile structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 234-248.
    3. Jiang, Rong & Zhou, Zhan-Gong & Qian, Wei-Min & Chen, Yong, 2013. "Two step composite quantile regression for single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 180-191.
    4. Harding, Matthew & Lamarche, Carlos, 2013. "Penalized Quantile Regression with Semiparametric Correlated Effects: Applications with Heterogeneous Preferences," IZA Discussion Papers 7741, Institute for the Study of Labor (IZA).

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