Share This Article:

Composite Quantile Regression for Nonparametric Model with Random Censored Data

Abstract Full-Text HTML XML Download Download as PDF (Size:459KB) PP. 65-73
DOI: 10.4236/ojs.2013.32009    4,320 Downloads   7,080 Views   Citations

ABSTRACT

The composite quantile regression should provide estimation efficiency gain over a single quantile regression. In this paper, we extend composite quantile regression to nonparametric model with random censored data. The asymptotic normality of the proposed estimator is established. The proposed methods are applied to the lung cancer data. Extensive simulations are reported, showing that the proposed method works well in practical settings.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

R. Jiang and W. Qian, "Composite Quantile Regression for Nonparametric Model with Random Censored Data," Open Journal of Statistics, Vol. 3 No. 2, 2013, pp. 65-73. doi: 10.4236/ojs.2013.32009.

References

[1] J. L. Powell, “Least Absolute Deviations Estimation for the Censored Regression Model,” Journal of Economet rics, Vol. 25, No. 3, 1984, pp. 303-325. doi:10.1016/0304-4076(84)90004-6
[2] S. Portnoy, “Censored Regression Quantiles,” Journal of the American Statistical Association, Vol. 98, No. 464, 2003, pp. 1001-1012. doi:10.1198/016214503000000954
[3] L. Peng and Y. Huang, “Survival Analysis with Quantile Regression Models,” Journal of the American Statistical Association, Vol. 103, No. 482, 2008, pp. 637-649. doi:10.1198/016214508000000355
[4] H. J. Wang and L. Wang, “Locally Weighted Censored Quantile Regression,” Journal of the American Statistical Association, Vol. 104, No. 478, 2009, pp. 1117-1128. doi:10.1198/jasa.2009.tm08230
[5] H. Zou and M. Yuan, “Composite Quantile Regression and the Oracle Model Selection Theory,” Annals of Statistics, Vol. 36, No. 3, 2008, pp. 1108-1126. doi:10.1214/07-AOS507
[6] B. Kai, R. Li and H. Zou, “Local Composite Quantile Regression Smoothing: An Efficient and Safe Alternative to Local Polynomial Regression,” Journal of the Royal Statistical Society, Series B, Vol. 72, No. 1, 2010, pp. 49 69. doi:10.1111/j.1467-9868.2009.00725.x
[7] B. Kai, R. Li and H. Zou, “New Efficient Estimation and Variable Selection Methods for Semiparametric Varying Coefficient Partially Linear Models,” Annals of Statistics, Vol. 39, No. 1, 2011, pp. 305-332. doi:10.1214/10-AOS842
[8] R. Jiang, Z. G. Zhou, W. M. Qian and W. Q. Shao, “Single-Index Composite Quantile Regression,” Journal of the Korean Statistical Society, Vol. 3, No. 3, 2012, pp. 323-332. doi:10.1016/j.jkss.2011.11.001
[9] R. Jiang, W. M. Qian and Z. G. Zhou, “Variable Selection and Coefficient Estimation via Composite Quantile Regression with Randomly Censored Data,” Statistics & Probability Letters, Vol. 2, No. 2, 2012, pp. 308-317. doi:10.1016/j.spl.2011.10.017
[10] A. Gannoun, J. Saracco, A. Yuan and G. Bonney, “Non Parametric Quantile Regression with Censored Data,” Scandinavian Journal of Statistics, Vol. 32, No. 4, 2005, pp. 527-550. doi:10.1111/j.1467-9469.2005.00456.x
[11] C. L. Loprinzi, et al., “Prospective Evaluation of Prognostic Variables from Patient-Completed Questionnaires. North Central Cancer Treatment Group,” Journal of Clinical Oncology, Vol. 12, No. 3, 1994, pp. 601-607.
[12] W. Gonzalez-Manteiga and C. Cadarso-Suarez, “Asymptotic Properties of a Generalized Kaplan-Meier Estimator with Some Applications,” Journal of Nonparametric Sta tistics, Vol. 4, No. 1, 1994, pp. 65-78. doi:10.1080/10485259408832601
[13] K. Knight, “Limiting Distributions for L1 Regression Estimators under General Conditions,” Annals of Statistics, Vol. 26, No. 2, 1998, pp. 755-770. doi:10.1214/aos/1028144858

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.