IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v64y2008i1p132-140.html
   My bibliography  Save this article

Doubly Penalized Buckley–James Method for Survival Data with High-Dimensional Covariates

Author

Listed:
  • Sijian Wang
  • Bin Nan
  • Ji Zhu
  • David G. Beer

Abstract

No abstract is available for this item.

Suggested Citation

  • Sijian Wang & Bin Nan & Ji Zhu & David G. Beer, 2008. "Doubly Penalized Buckley–James Method for Survival Data with High-Dimensional Covariates," Biometrics, The International Biometric Society, vol. 64(1), pages 132-140, March.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:1:p:132-140
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2007.00877.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Jie Huang & David Harrington, 2005. "Iterative Partial Least Squares with Right-Censored Data Analysis: A Comparison to Other Dimension Reduction Techniques," Biometrics, The International Biometric Society, vol. 61(1), pages 17-24, March.
    2. Shuangge Ma & Michael R. Kosorok & Jason P. Fine, 2006. "Additive Risk Models for Survival Data with High-Dimensional Covariates," Biometrics, The International Biometric Society, vol. 62(1), pages 202-210, March.
    3. Bin Nan & Xihong Lin & Lynda D. Lisabeth & Siobán D. Harlow, 2005. "A Varying-Coefficient Cox Model for the Effect of Age at a Marker Event on Age at Menopause," Biometrics, The International Biometric Society, vol. 61(2), pages 576-583, June.
    4. 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.
    5. 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.
    6. 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.
    7. Jie Huang & David Harrington, 2002. "Penalized Partial Likelihood Regression for Right-Censored Data with Bootstrap Selection of the Penalty Parameter," Biometrics, The International Biometric Society, vol. 58(4), pages 781-791, December.
    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. Fei Liu & David Dunson & Fei Zou, 2011. "High-Dimensional Variable Selection in Meta-Analysis for Censored Data," Biometrics, The International Biometric Society, vol. 67(2), pages 504-512, June.
    2. K. Hendrickx & P. Janssen & A. Verhasselt, 2018. "Penalized spline estimation in varying coefficient models with censored data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 871-895, December.
    3. Engler David & Li Yi, 2009. "Survival Analysis with High-Dimensional Covariates: An Application in Microarray Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-24, February.
    4. Zhang, Mimi & Hu, Qingpei & Xie, Min & Yu, Dan, 2014. "Lower confidence limit for reliability based on grouped data using a quantile-filling algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 96-111.
    5. Ying Ding & Bin Nan, 2015. "Estimating Mean Survival Time: When is it Possible?," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 397-413, June.
    6. 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.
    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. Wenjing Yin & Sihai Dave Zhao & Feng Liang, 2022. "Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 282-318, April.
    9. Xingwei Tong & Xin He & Liuquan Sun & Jianguo Sun, 2009. "Variable Selection for Panel Count Data via Non‐Concave Penalized Estimating Function," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 620-635, December.
    10. T. Cai & J. Huang & L. Tian, 2009. "Regularized Estimation for the Accelerated Failure Time Model," Biometrics, The International Biometric Society, vol. 65(2), pages 394-404, June.
    11. 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.
    12. 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).
    13. Li Liu & Hao Wang & Yanyan Liu & Jian Huang, 2021. "Model pursuit and variable selection in the additive accelerated failure time model," Statistical Papers, Springer, vol. 62(6), pages 2627-2659, December.
    14. Sariyar Murat & Schumacher Martin & Binder Harald, 2014. "A boosting approach for adapting the sparsity of risk prediction signatures based on different molecular levels," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(3), pages 343-357, June.
    15. Yan, Xiaodong & Wang, Hongni & Wang, Wei & Xie, Jinhan & Ren, Yanyan & Wang, Xinjun, 2021. "Optimal model averaging forecasting in high-dimensional survival analysis," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1147-1155.
    16. G. Yi & J. Q. Shi & T. Choi, 2011. "Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data," Biometrics, The International Biometric Society, vol. 67(4), pages 1285-1294, December.
    17. J. Choi & S. Ye & K. H. Eng & K. Korthauer & W. H. Bradley & J. S. Rader & C. Kendziorski, 2017. "IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 1-12, June.

    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. 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.
    2. T. Cai & J. Huang & L. Tian, 2009. "Regularized Estimation for the Accelerated Failure Time Model," Biometrics, The International Biometric Society, vol. 65(2), pages 394-404, June.
    3. 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.
    4. Engler David & Li Yi, 2009. "Survival Analysis with High-Dimensional Covariates: An Application in Microarray Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-24, February.
    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. Lee, Kyu Ha & Chakraborty, Sounak & Sun, Jianguo, 2017. "Variable selection for high-dimensional genomic data with censored outcomes using group lasso prior," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 1-13.
    7. Yang, Yuan & McMahan, Christopher S. & Wang, Yu-Bo & Ouyang, Yuyuan, 2024. "Estimation of l0 norm penalized models: A statistical treatment," Computational Statistics & Data Analysis, Elsevier, vol. 192(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. Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank.
    10. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    11. Hou-Tai Chang & Ping-Huai Wang & Wei-Fang Chen & Chen-Ju Lin, 2022. "Risk Assessment of Early Lung Cancer with LDCT and Health Examinations," IJERPH, MDPI, vol. 19(8), pages 1-12, April.
    12. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
    13. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    14. Wang, Qiao & Zhou, Wei & Cheng, Yonggang & Ma, Gang & Chang, Xiaolin & Miao, Yu & Chen, E, 2018. "Regularized moving least-square method and regularized improved interpolating moving least-square method with nonsingular moment matrices," Applied Mathematics and Computation, Elsevier, vol. 325(C), pages 120-145.
    15. Dmitriy Drusvyatskiy & Adrian S. Lewis, 2018. "Error Bounds, Quadratic Growth, and Linear Convergence of Proximal Methods," Mathematics of Operations Research, INFORMS, vol. 43(3), pages 919-948, August.
    16. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    17. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    18. Candelon, B. & Hurlin, C. & Tokpavi, S., 2012. "Sampling error and double shrinkage estimation of minimum variance portfolios," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 511-527.
    19. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    20. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Specification Choices in Quantile Regression for Empirical Macroeconomics," Working Papers 22-25, Federal Reserve Bank of Cleveland.

    More about this item

    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:bla:biomet:v:64:y:2008:i:1:p:132-140. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.