Skip to main content
Log in

Feature Screening for High-Dimensional Survival Data via Censored Quantile Correlation

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

This paper proposes a new sure independence screening procedure for high-dimensional survival data based on censored quantile correlation (CQC). This framework has two distinctive features: 1) Via incorporating a weighting scheme, our metric is a natural extension of quantile correlation (QC), considered by Li (2015), to handle high-dimensional survival data; 2) The proposed method not only is robust against outliers, but also can discover the nonlinear relationship between independent variables and censored dependent variable. Additionally, the proposed method enjoys the sure screening property under certain technical conditions. Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Fan J and Lü J, Sure independence screening for ultrahigh dimensional feature space (with discussion), Journal of the Royal Statistical Society, Series B, 2008, 70: 849–911.

    Article  MathSciNet  Google Scholar 

  2. Zhu L, Li L, Li R, et al., Model-Free feature screening for ultrahigh dimensional data, Journal of the American Statistical Association, 2011, 106: 1464–1475.

    Article  MathSciNet  Google Scholar 

  3. Li R, Zhong W, and Zhu L, Feature screening via distance correlation learning, Journal of the American Statistical Association, 2012, 107: 1129–1139.

    Article  MathSciNet  Google Scholar 

  4. He X, Wang L, and Hong H, Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data, The Annals of Statistics, 2013, 41: 342–369.

    Article  MathSciNet  Google Scholar 

  5. Shao X and Zhang J, Martingale difference correlation and its use in high dimensional variable screening, Journal of the American Statistical Association, 2014, 109: 1302–1318.

    Article  MathSciNet  Google Scholar 

  6. Fan J, Feng Y, and Wu Y, Ultrahigh dimensional variable selection for Cox’s proportional hazards model, IMS Collections, 2010, 6: 70–86.

    Google Scholar 

  7. Zhao S and Li Y, Principled sure independence screening for Cox models with ultrahigh-dimensional covariates, Journal of Multivariate Analysis, 2012, 105: 397–411.

    Article  MathSciNet  Google Scholar 

  8. Song R, Lu W, Ma S, et al., Censored rank independence screening for high-dimensional survival data, Biometrika, 2014, 101: 799–814.

    Article  MathSciNet  Google Scholar 

  9. Wu Y and Yin G, Conditional quantile screening in ultrahigh-dimensional heterogeneous data, Biometrika, 2015, 102: 65–76.

    Article  MathSciNet  Google Scholar 

  10. Zhou T and Zhu L, Model-free feature screening for ultrahigh dimensional censored regression, Statistics and Computing, 2017, 27: 947–961.

    Article  MathSciNet  Google Scholar 

  11. Li G, Li Y, and Tsai C, Quantile correlations and quantile autoregressive modeling, Journal of the American Statistical Association, 2015, 110: 246–261.

    Article  MathSciNet  Google Scholar 

  12. Ma X and Zhang J, Robust model-free feature screening via quantile correlation, Journal of Multivariate Analysis, 2016, 143: 472–480.

    Article  MathSciNet  Google Scholar 

  13. Wang J and Wang L, Locally weighted censored quantile regression, Journal of the American Statistical Association, 2009, 104: 1117–28.

    Article  MathSciNet  Google Scholar 

  14. Zou H and Yuan M, Composite quantile regression and the oracle model selection theory, The Annals of Statistics, 2008, 36: 1108–1126.

    Article  MathSciNet  Google Scholar 

  15. Serfling R L, Approximation Theorems in Mathematical Statistics, John Wiley & Sons Inc, New York, 1980.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kai Xu.

Additional information

This work is supported by the National Natural Science Foundation of China under Grant No. 11901006 and the Natural Science Foundation of Anhui Province under Grant Nos. 1908085QA06 and 1908085MA20.

This paper was recommended for publication by Editor ZHU Liping.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, K., Huang, X. Feature Screening for High-Dimensional Survival Data via Censored Quantile Correlation. J Syst Sci Complex 34, 1207–1224 (2021). https://doi.org/10.1007/s11424-020-9295-5

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-020-9295-5

Keywords

Navigation