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Self-consistent estimation of censored quantile regression

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  • Peng, Limin
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    Abstract

    The principle of self-consistency has been employed to estimate regression quantile with randomly censored response. The asymptotic studies for this type of approach was established only recently, partly due to the complex forms of the current self-consistent estimators of censored regression quantiles. Of interest, how the self-consistent estimation of censored regression quantiles is connected to the alternative martingale-based approach still remains uncovered. In this paper, we propose a new formulation of self-consistent censored regression quantiles based on stochastic integral equations. The proposed representation of censored regression quantiles entails a clearly defined estimation procedure. More importantly, it greatly simplifies the theoretical investigations. We establish the large sample equivalence between the proposed self-consistent estimators and the existing estimator derived from martingale-based estimating equations. The connection between the new self-consistent estimation approach and the available self-consistent algorithms is also elaborated.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 105 (2012)
    Issue (Month): 1 ()
    Pages: 368-379

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    Handle: RePEc:eee:jmvana:v:105:y:2012:i:1:p:368-379

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    Related research

    Keywords: Censoring; Martingale; Regression quantile; Self-consistency; Stochastic integral equation;

    References

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    1. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    2. 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.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Neocleous, Tereza & Portnoy, Stephen, 2008. "On monotonicity of regression quantile functions," Statistics & Probability Letters, Elsevier, vol. 78(10), pages 1226-1229, August.
    5. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
    6. Honore, Bo & Khan, Shakeeb & Powell, James L., 2002. "Quantile regression under random censoring," Journal of Econometrics, Elsevier, vol. 109(1), pages 67-105, July.
    7. 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.
    8. Roger Koenker, . "Censored Quantile Regression Redux," Journal of Statistical Software, American Statistical Association, vol. 27(i06).
    9. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
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