IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v181y2022ics0167715221002376.html
   My bibliography  Save this article

Reproducible feature selection in high-dimensional accelerated failure time models

Author

Listed:
  • Dong, Yan
  • Li, Daoji
  • Zheng, Zemin
  • Zhou, Jia

Abstract

We propose a new feature selection procedure with guaranteed FDR control for high-dimensional AFT models, which is among the first attempts of reproducible learning in survival analysis. The effectiveness of the proposed method is theoretically and numerically demonstrated.

Suggested Citation

  • Dong, Yan & Li, Daoji & Zheng, Zemin & Zhou, Jia, 2022. "Reproducible feature selection in high-dimensional accelerated failure time models," Statistics & Probability Letters, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:stapro:v:181:y:2022:i:c:s0167715221002376
    DOI: 10.1016/j.spl.2021.109275
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715221002376
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2021.109275?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jian Huang & Shuangge Ma & Huiliang Xie, 2006. "Regularized Estimation in the Accelerated Failure Time Model with High-Dimensional Covariates," Biometrics, The International Biometric Society, vol. 62(3), pages 813-820, September.
    2. Anders Gorst-Rasmussen & Thomas Scheike, 2013. "Independent screening for single-index hazard rate models with ultrahigh dimensional features," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 217-246, March.
    3. Yingying Fan & Emre Demirkaya & Gaorong Li & Jinchi Lv, 2020. "RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 362-379, January.
    4. Stute, W., 1993. "Consistent Estimation Under Random Censorship When Covariables Are Present," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 89-103, April.
    5. Emmanuel Candès & Yingying Fan & Lucas Janson & Jinchi Lv, 2018. "Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 551-577, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pan, Yingli, 2022. "Feature screening and FDR control with knockoff features for ultrahigh-dimensional right-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    2. Emre Demirkaya & Yang Feng & Pallavi Basu & Jinchi Lv, 2022. "Large-scale model selection in misspecified generalized linear models [Information theory and an extension of the maximum likelihood principle]," Biometrika, Biometrika Trust, vol. 109(1), pages 123-136.
    3. Wang Zhu & Wang C.Y., 2010. "Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-33, June.
    4. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    5. Xiaochao Xia & Binyan Jiang & Jialiang Li & Wenyang Zhang, 2016. "Low-dimensional confounder adjustment and high-dimensional penalized estimation for survival analysis," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(4), pages 547-569, October.
    6. Zhihua Sun & Yi Liu & Kani Chen & Gang Li, 2022. "Broken adaptive ridge regression for right-censored survival data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(1), pages 69-91, February.
    7. Khan Md Hasinur Rahaman & Bhadra Anamika & Howlader Tamanna, 2019. "Stability selection for lasso, ridge and elastic net implemented with AFT models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(5), pages 1-14, October.
    8. Ma, Shuangge & Dai, Ying & Huang, Jian & Xie, Yang, 2012. "Identification of breast cancer prognosis markers via integrative analysis," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2718-2728.
    9. Hu, Jianwei & Chai, Hao, 2013. "Adjusted regularized estimation in the accelerated failure time model with high dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 96-114.
    10. Wen, Xin & Li, Yang & Zheng, Zemin, 2024. "Scalable efficient reproducible multi-task learning via data splitting," Statistics & Probability Letters, Elsevier, vol. 208(C).
    11. Dong, Qingkai & Liu, Binxia & Zhao, Hui, 2023. "Weighted least squares model averaging for accelerated failure time models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    12. Haixiang Zhang & Jian Huang & Liuquan Sun, 2022. "Projection‐based and cross‐validated estimation in high‐dimensional Cox model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 353-372, March.
    13. Jing Zhang & Qihua Wang & Xuan Wang, 2022. "Surrogate-variable-based model-free feature screening for survival data under the general censoring mechanism," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 379-397, April.
    14. Jacobo de Uña-Álvarez & Luís Meira-Machado, 2015. "Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study," Biometrics, The International Biometric Society, vol. 71(2), pages 364-375, June.
    15. Liang, Weijuan & Zhang, Qingzhao & Ma, Shuangge, 2024. "Hierarchical false discovery rate control for high-dimensional survival analysis with interactions," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    16. Olivier Lopez & Xavier Milhaud & Pierre-Emmanuel Thérond, 2016. "Tree-based censored regression with applications in insurance," Post-Print hal-01364437, HAL.
    17. Zhiping Qiu & Jing Qin & Yong Zhou, 2016. "Composite Estimating Equation Method for the Accelerated Failure Time Model with Length-biased Sampling Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 396-415, June.
    18. Jing Zhang & Haibo Zhou & Yanyan Liu & Jianwen Cai, 2021. "Conditional screening for ultrahigh-dimensional survival data in case-cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 632-661, October.
    19. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2023. "Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 235(1), pages 166-179.
    20. Bao, Yanchun & He, Shuyuan & Mei, Changlin, 2007. "The Koul-Susarla-Van Ryzin and weighted least squares estimates for censored linear regression model: A comparative study," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6488-6497, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:181:y:2022:i:c:s0167715221002376. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.