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Variable selection for general transformation models with right censored data via nonconcave penalties

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  • Li, Jianbo
  • Gu, Minggao
  • Zhang, Riquan

Abstract

In this paper, we consider variable selection for general transformation models with right censored data via nonconcave penalties. We will conduct the variable selection by maximizing the penalized log-marginal likelihood function. In the proposed variable selection procedures, we not only can select significant variables and but also are able to estimate corresponding effects simultaneously. With proper penalties and some conditions, we show that the resulting penalized estimates are consistent and enjoy oracle properties. We will illustrate our proposed variable selection procedures through some simulation studies and a real data application.

Suggested Citation

  • Li, Jianbo & Gu, Minggao & Zhang, Riquan, 2013. "Variable selection for general transformation models with right censored data via nonconcave penalties," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 445-456.
  • Handle: RePEc:eee:jmvana:v:115:y:2013:i:c:p:445-456
    DOI: 10.1016/j.jmva.2012.11.002
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    References listed on IDEAS

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    1. Fushing Hsieh, 2001. "On heteroscedastic hazards regression models: theory and application," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 63-79.
    2. Antoniadis, Anestis & Fryzlewicz, Piotr & Letué, Frédérique, 2010. "The Dantzig selector in Cox's proportional hazards model," LSE Research Online Documents on Economics 30992, London School of Economics and Political Science, LSE Library.
    3. 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.
    4. A. Antoniadis, 1997. "Wavelets in statistics: A review," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 6(2), pages 97-130, August.
    5. Anestis Antoniadis & Piotr Fryzlewicz & Frédérique Letué, 2010. "The Dantzig Selector in Cox's Proportional Hazards Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 531-552, December.
    6. Antoniadis A. & Fan J., 2001. "Regularization of Wavelet Approximations," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 939-967, September.
    7. 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.
    8. 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.
    9. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
    10. Li, Jianbo & Gu, Minggao, 2012. "Adaptive LASSO for general transformation models with right censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2583-2597.
    11. Jianbo Li & Minggao Gu & Tao Hu, 2012. "General partially linear varying-coefficient transformation models for ranking data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1475-1488, January.
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