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LAD variable selection for linear models with randomly censored data

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  • Zhangong Zhou

    ()

  • Rong Jiang

    ()

  • Weimin Qian

    ()

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    Abstract

    The least absolute deviations (LAD) variable selection for linear models with randomly censored data is studied through the Lasso. The proposed procedure can select significant variables in the parameters. With appropriate selection of the tuning parameters, we establish the consistency of this procedure and the oracle property of the resulting estimators. Simulation studies are conducted to compare the proposed procedure with an inverse-censoring-probability weighted LAD LASSO-estimator. Copyright Springer-Verlag 2013

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    File URL: http://hdl.handle.net/10.1007/s00184-012-0387-7
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    Bibliographic Info

    Article provided by Springer in its journal Metrika.

    Volume (Year): 76 (2013)
    Issue (Month): 2 (February)
    Pages: 287-300

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    Handle: RePEc:spr:metrik:v:76:y:2013:i:2:p:287-300

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    Web page: http://www.springerlink.com/link.asp?id=102509

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    Related research

    Keywords: LAD-LASSO; Linear regression; Randomly censored data; Oracle property;

    References

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    1. Hansheng Wang & Runze Li & Chih-Ling Tsai, 2007. "Tuning parameter selectors for the smoothly clipped absolute deviation method," Biometrika, Biometrika Trust, vol. 94(3), pages 553-568.
    2. Johnson, Brent A. & Lin, D.Y. & Zeng, Donglin, 2008. "Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 672-680, June.
    3. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Zhang, Hao Helen & Lu, Wenbin & Wang, Hansheng, 2010. "On sparse estimation for semiparametric linear transformation models," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1594-1606, August.
    6. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    7. Jianqing Fan & Runze Li, 2004. "New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 710-723, January.
    8. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    9. Zhou, Xiuqing & Wang, Jinde, 2005. "A genetic method of LAD estimation for models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 451-466, March.
    10. Qin, Gengsheng & Tsao, Min, 2003. "Empirical likelihood inference for median regression models for censored survival data," Journal of Multivariate Analysis, Elsevier, vol. 85(2), pages 416-430, May.
    11. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-58, March.
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