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Variable selection and coefficient estimation via composite quantile regression with randomly censored data

  • Jiang, Rong
  • Qian, Weimin
  • Zhou, Zhangong
Registered author(s):

    Composite quantile regression with randomly censored data is studied. Moreover, adaptive LASSO methods for composite quantile regression with randomly censored data are proposed. The consistency, asymptotic normality and oracle property of the proposed estimators are established. The proposals are illustrated via simulation studies and the Australian AIDS dataset.

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    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 82 (2012)
    Issue (Month): 2 ()
    Pages: 308-317

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    Handle: RePEc:eee:stapro:v:82:y:2012:i:2:p:308-317
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    9. Wang, Hansheng & Li, Guodong & Jiang, Guohua, 2007. "Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 347-355, July.
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