IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i5p763-d760254.html
   My bibliography  Save this article

Variable Selection for Generalized Linear Models with Interval-Censored Failure Time Data

Author

Listed:
  • Rong Liu

    (Center for Applied Statistical Research, College of Mathematics, Jilin University, Changchun 130012, China
    National Applied Mathematical Center (Jilin), Changchun 130012, China)

  • Shishun Zhao

    (Center for Applied Statistical Research, College of Mathematics, Jilin University, Changchun 130012, China
    National Applied Mathematical Center (Jilin), Changchun 130012, China)

  • Tao Hu

    (School of Mathematical Sciences, Capital Normal University, Beijing 100048, China)

  • Jianguo Sun

    (Department of Statistics, University of Missouri, Columbia, MO 65211, USA)

Abstract

Variable selection is often needed in many fields and has been discussed by many authors in various situations. This is especially the case under linear models and when one observes complete data. Among others, one common situation where variable selection is required is to identify important risk factors from a large number of covariates. In this paper, we consider the problem when one observes interval-censored failure time data arising from generalized linear models, for which there does not seem to exist an established method. To address this, we propose a penalized least squares method with the use of an unbiased transformation and the oracle property of the method is established along with the asymptotic normality of the resulting estimators of regression parameters. Simulation studies were conducted and demonstrated that the proposed method performed well for practical situations. In addition, the method was applied to a motivating example about children’s mortality data of Nigeria.

Suggested Citation

  • Rong Liu & Shishun Zhao & Tao Hu & Jianguo Sun, 2022. "Variable Selection for Generalized Linear Models with Interval-Censored Failure Time Data," Mathematics, MDPI, vol. 10(5), pages 1-18, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:763-:d:760254
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/5/763/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/5/763/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Jianwen Cai & Jianqing Fan & Runze Li & Haibo Zhou, 2005. "Variable selection for multivariate failure time data," Biometrika, Biometrika Trust, vol. 92(2), pages 303-316, June.
    3. Shuwei Li & Tao Hu & Peijie Wang & Jianguo Sun, 2018. "A Class of Semiparametric Transformation Models for Doubly Censored Failure Time Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(3), pages 682-698, September.
    4. 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.
    5. Xia Zheng & Yaohua Rong & Ling Liu & Weihu Cheng, 2021. "A More Accurate Estimation of Semiparametric Logistic Regression," Mathematics, MDPI, vol. 9(19), pages 1-12, September.
    6. Hui Zhao & Qiwei Wu & Gang Li & Jianguo Sun, 2020. "Simultaneous Estimation and Variable Selection for Interval-Censored Data With Broken Adaptive Ridge Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 204-216, January.
    7. Peijie Wang & Hui Zhao & Jianguo Sun, 2016. "Regression analysis of case K interval‐censored failure time data in the presence of informative censoring," Biometrics, The International Biometric Society, vol. 72(4), pages 1103-1112, December.
    8. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    9. Kneib, Thomas, 2006. "Mixed model-based inference in geoadditive hazard regression for interval-censored survival times," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 777-792, November.
    10. Dai, Linlin & Chen, Kani & Sun, Zhihua & Liu, Zhenqiu & Li, Gang, 2018. "Broken adaptive ridge regression and its asymptotic properties," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 334-351.
    11. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lu, Huimin & Wang, Yilong & Bing, Heming & Wang, Shuying & Li, Niya, 2025. "Efficient regularized estimation of graphical proportional hazards model with interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 209(C).
    2. Bo Zhao & Shuying Wang & Chunjie Wang, 2024. "Variable selection in proportional odds model with informatively interval-censored data," Statistical Papers, Springer, vol. 65(4), pages 2461-2488, June.
    3. Hongchang Hu & Mingqiu Liu & Zhen Zeng, 2025. "Penalized Lq-likelihood estimator and its influence function in generalized linear models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 88(1), pages 1-18, January.

    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. Bo Zhao & Shuying Wang & Chunjie Wang, 2024. "Variable selection in proportional odds model with informatively interval-censored data," Statistical Papers, Springer, vol. 65(4), pages 2461-2488, June.
    2. Du, Mingyue & Zhao, Xingqiu, 2024. "A conditional approach for regression analysis of case K interval-censored failure time data with informative censoring," Computational Statistics & Data Analysis, Elsevier, vol. 198(C).
    3. Takumi Saegusa & Tianzhou Ma & Gang Li & Ying Qing Chen & Mei-Ling Ting Lee, 2020. "Variable Selection in Threshold Regression Model with Applications to HIV Drug Adherence Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(3), pages 376-398, December.
    4. Belli, Edoardo, 2022. "Smoothly adaptively centered ridge estimator," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    5. 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.
    6. Kaida Cai & Hua Shen & Xuewen Lu, 2022. "Adaptive bi-level variable selection for multivariate failure time model with a diverging number of covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 968-993, December.
    7. Du, Mingyue & Zhao, Xingqiu & Sun, Jianguo, 2022. "Variable selection for case-cohort studies with informatively interval-censored outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    8. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    9. Xu, Yang & Zhao, Shishun & Hu, Tao & Sun, Jianguo, 2021. "Variable selection for generalized odds rate mixture cure models with interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    10. Umberto Amato & Anestis Antoniadis & Italia De Feis & Irene Gijbels, 2021. "Penalised robust estimators for sparse and high-dimensional linear models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 1-48, March.
    11. Wang, Christina Dan & Chen, Zhao & Lian, Yimin & Chen, Min, 2022. "Asset selection based on high frequency Sharpe ratio," Journal of Econometrics, Elsevier, vol. 227(1), pages 168-188.
    12. Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(3), pages 267-286.
    13. Peter Bühlmann & Jacopo Mandozzi, 2014. "High-dimensional variable screening and bias in subsequent inference, with an empirical comparison," Computational Statistics, Springer, vol. 29(3), pages 407-430, June.
    14. Capanu, Marinela & Giurcanu, Mihai & Begg, Colin B. & Gönen, Mithat, 2023. "Subsampling based variable selection for generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    15. Ander Wilson & Brian J. Reich, 2014. "Confounder selection via penalized credible regions," Biometrics, The International Biometric Society, vol. 70(4), pages 852-861, December.
    16. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    17. Zeyu Bian & Erica E. M. Moodie & Susan M. Shortreed & Sahir Bhatnagar, 2023. "Variable selection in regression‐based estimation of dynamic treatment regimes," Biometrics, The International Biometric Society, vol. 79(2), pages 988-999, June.
    18. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    19. Christidis, Anthony-Alexander & Van Aelst, Stefan & Zamar, Ruben, 2025. "Multi-model subset selection," Computational Statistics & Data Analysis, Elsevier, vol. 203(C).
    20. Simona Buscemi & Antonella Plaia, 2020. "Model selection in linear mixed-effect models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 529-575, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jmathe:v:10:y:2022:i:5:p:763-:d:760254. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.