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Adaptive Lasso for Cox's proportional hazards model

Citations

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Cited by:

  1. Liuquan Sun & Shuwei Li & Lianming Wang & Xinyuan Song & Xuemei Sui, 2022. "Simultaneous variable selection in regression analysis of multivariate interval‐censored data," Biometrics, The International Biometric Society, vol. 78(4), pages 1402-1413, December.
  2. Jun Yan & Jian Huang, 2012. "Model Selection for Cox Models with Time-Varying Coefficients," Biometrics, The International Biometric Society, vol. 68(2), pages 419-428, June.
  3. Hess, Wolfgang & Persson, Maria & Rubenbauer, Stephanie & Gertheiss, Jan, 2013. "Using Lasso-Type Penalties to Model Time-Varying Covariate Effects in Panel Data Regressions – A Novel Approach Illustrated by the ‘Death of Distance’ in International Trade," Working Paper Series 961, Research Institute of Industrial Economics.
  4. Fengting Yi & Niansheng Tang & Jianguo Sun, 2022. "Simultaneous variable selection and estimation for joint models of longitudinal and failure time data with interval censoring," Biometrics, The International Biometric Society, vol. 78(1), pages 151-164, March.
  5. 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).
  6. repec:hum:wpaper:sfb649dp2012-061 is not listed on IDEAS
  7. Kevin He & Yue Wang & Xiang Zhou & Han Xu & Can Huang, 2019. "An improved variable selection procedure for adaptive Lasso in high-dimensional survival analysis," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 569-585, July.
  8. Zhixuan Fu & Chirag R. Parikh & Bingqing Zhou, 2017. "Penalized variable selection in competing risks regression," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 353-376, July.
  9. Seung Jun Shin & Yichao Wu & Hao Helen Zhang & Yufeng Liu, 2014. "Probability-enhanced sufficient dimension reduction for binary classification," Biometrics, The International Biometric Society, vol. 70(3), pages 546-555, September.
  10. 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.
  11. Yu Zheng & Tianxi Cai, 2017. "Augmented estimation for t‐year survival with censored regression models," Biometrics, The International Biometric Society, vol. 73(4), pages 1169-1178, December.
  12. Fan Feng & Guanghui Cheng & Jianguo Sun, 2023. "Variable Selection for Length-Biased and Interval-Censored Failure Time Data," Mathematics, MDPI, vol. 11(22), pages 1-20, November.
  13. Ling Zhou & Lu Tang & Angela T. Song & Diane M. Cibrik & Peter X.-K. Song, 2017. "A LASSO Method to Identify Protein Signature Predicting Post-transplant Renal Graft Survival," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 431-452, December.
  14. Suiyao Chen & Nan Kong & Xuxue Sun & Hongdao Meng & Mingyang Li, 2019. "Claims data-driven modeling of hospital time-to-readmission risk with latent heterogeneity," Health Care Management Science, Springer, vol. 22(1), pages 156-179, March.
  15. Qu, Lianqiang & Wang, Xiaoyu & Sun, Liuquan, 2022. "Variable screening for varying coefficient models with ultrahigh-dimensional survival data," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
  16. Tang, Linjun & Zhou, Zhangong & Wu, Changchun, 2012. "Weighted composite quantile estimation and variable selection method for censored regression model," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 653-663.
  17. Zhang, Tao & Zhang, Qingzhao & Wang, Qihua, 2014. "Model detection for functional polynomial regression," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 183-197.
  18. Yichao Wu, 2011. "An ordinary differential equation-based solution path algorithm," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(1), pages 185-199.
  19. Wei Wang & Shou‐En Lu & Jerry Q. Cheng & Minge Xie & John B. Kostis, 2022. "Multivariate survival analysis in big data: A divide‐and‐combine approach," Biometrics, The International Biometric Society, vol. 78(3), pages 852-866, September.
  20. Bergersen Linn Cecilie & Glad Ingrid K. & Lyng Heidi, 2011. "Weighted Lasso with Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-29, August.
  21. Hao, Meiling & Lin, Yunyuan & Zhao, Xingqiu, 2016. "A relative error-based approach for variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 250-262.
  22. Honda, Toshio & Yabe, Ryota, 2017. "Variable selection and structure identification for varying coefficient Cox models," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 103-122.
  23. Xin Cheng & Wenbin Lu & Mengling Liu, 2015. "Identification of homogeneous and heterogeneous variables in pooled cohort studies," Biometrics, The International Biometric Society, vol. 71(2), pages 397-403, June.
  24. Pötscher, Benedikt M., 2007. "Confidence Sets Based on Sparse Estimators Are Necessarily Large," MPRA Paper 5677, University Library of Munich, Germany.
  25. Kexuan Li, 2025. "Variable Selection for Nonlinear Cox Regression Model via Deep Learning," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 12(1), pages 1-21, January.
  26. Jiang Du & Zhongzhan Zhang & Tianfa Xie, 2017. "Focused information criterion and model averaging in censored quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(5), pages 547-570, July.
  27. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
  28. Young Joo Yoon & Cheolwoo Park & Erik Hofmeister & Sangwook Kang, 2012. "Group variable selection in cardiopulmonary cerebral resuscitation data for veterinary patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1605-1621, January.
  29. Joseph G. Ibrahim & Hongtu Zhu & Ramon I. Garcia & Ruixin Guo, 2011. "Fixed and Random Effects Selection in Mixed Effects Models," Biometrics, The International Biometric Society, vol. 67(2), pages 495-503, June.
  30. Hu, Yuao & Lian, Heng, 2013. "Variable selection in a partially linear proportional hazards model with a diverging dimensionality," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 61-69.
  31. Brown Andrew Anand & Richardson Sylvia & Whittaker John, 2011. "Application of the Lasso to Expression Quantitative Trait Loci Mapping," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-35, March.
  32. 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).
  33. Zhao, Sihai Dave & Li, Yi, 2012. "Principled sure independence screening for Cox models with ultra-high-dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 397-411.
  34. Denis Agniel & Katherine P. Liao & Tianxi Cai, 2016. "Estimation and testing for multiple regulation of multivariate mixed outcomes," Biometrics, The International Biometric Society, vol. 72(4), pages 1194-1205, December.
  35. Li‐Pang Chen & Grace Y. Yi, 2021. "Analysis of noisy survival data with graphical proportional hazards measurement error models," Biometrics, The International Biometric Society, vol. 77(3), pages 956-969, September.
  36. Honda, Toshio & Härdle, Wolfgang Karl, 2012. "Variable selection in Cox regression models with varying coefficients," SFB 649 Discussion Papers 2012-061, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  37. Badri Padhukasahasram & Chandan K Reddy & Yan Li & David E Lanfear, 2015. "Joint Impact of Clinical and Behavioral Variables on the Risk of Unplanned Readmission and Death after a Heart Failure Hospitalization," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-11, June.
  38. Xianyi Wu & Xian Zhou, 2019. "On Hodges’ superefficiency and merits of oracle property in model selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1093-1119, October.
  39. Na You & Shun He & Xueqin Wang & Junxian Zhu & Heping Zhang, 2018. "Subtype classification and heterogeneous prognosis model construction in precision medicine," Biometrics, The International Biometric Society, vol. 74(3), pages 814-822, September.
  40. 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.
  41. Xie Xiaodong & Zheng Shaozhi, 2017. "Group MCP for Cox Models with Time-Varying Coefficients," Journal of Systems Science and Information, De Gruyter, vol. 4(5), pages 476-488, October.
  42. Jianbo Li & Yuan Li & Riquan Zhang, 2017. "B spline variable selection for the single index models," Statistical Papers, Springer, vol. 58(3), pages 691-706, September.
  43. Yang, Guangren & Zhang, Ling & Li, Runze & Huang, Yuan, 2019. "Feature screening in ultrahigh-dimensional varying-coefficient Cox model," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 284-297.
  44. Wang, Shixuan & Syntetos, Aris A. & Liu, Ying & Di Cairano-Gilfedder, Carla & Naim, Mohamed M., 2023. "Improving automotive garage operations by categorical forecasts using a large number of variables," European Journal of Operational Research, Elsevier, vol. 306(2), pages 893-908.
  45. Wang, Lu & Shen, Jincheng & Thall, Peter F., 2014. "A modified adaptive Lasso for identifying interactions in the Cox model with the heredity constraint," Statistics & Probability Letters, Elsevier, vol. 93(C), pages 126-133.
  46. Zhixuan Fu & Shuangge Ma & Haiqun Lin & Chirag R. Parikh & Bingqing Zhou, 2017. "Penalized Variable Selection for Multi-center Competing Risks Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 379-405, December.
  47. Shan Luo & Jinfeng Xu & Zehua Chen, 2015. "Extended Bayesian information criterion in the Cox model with a high-dimensional feature space," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(2), pages 287-311, April.
  48. Chen, Xiaolin & Wang, Qihua, 2013. "Variable selection in the additive rate model for recurrent event data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 491-503.
  49. Hansheng Wang & Bo Li & Chenlei Leng, 2009. "Shrinkage tuning parameter selection with a diverging number of parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 671-683, June.
  50. Lu, Shuiyun & Chen, Xiaolin & Xu, Sheng & Liu, Chunling, 2020. "Joint model-free feature screening for ultra-high dimensional semi-competing risks data," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
  51. Wenbin Lu & Lexin Li, 2011. "Sufficient Dimension Reduction for Censored Regressions," Biometrics, The International Biometric Society, vol. 67(2), pages 513-523, June.
  52. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.
  53. Guang Cheng & Hao Zhang & Zuofeng Shang, 2015. "Sparse and efficient estimation for partial spline models with increasing dimension," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(1), pages 93-127, February.
  54. Lian, Heng & Li, Jianbo & Hu, Yuao, 2013. "Shrinkage variable selection and estimation in proportional hazards models with additive structure and high dimensionality," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 99-112.
  55. Qu, Lianqiang & Song, Xinyuan & Sun, Liuquan, 2018. "Identification of local sparsity and variable selection for varying coefficient additive hazards models," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 119-135.
  56. Heng Lian & Xin Chen & Jian-Yi Yang, 2012. "Identification of Partially Linear Structure in Additive Models with an Application to Gene Expression Prediction from Sequences," Biometrics, The International Biometric Society, vol. 68(2), pages 437-445, June.
  57. 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.
  58. Lian, Heng & Li, Jianbo & Tang, Xingyu, 2014. "SCAD-penalized regression in additive partially linear proportional hazards models with an ultra-high-dimensional linear part," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 50-64.
  59. Michael R. Wierzbicki & Li-Bing Guo & Qing-Tao Du & Wensheng Guo, 2014. "Sparse Semiparametric Nonlinear Model With Application to Chromatographic Fingerprints," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1339-1349, December.
  60. 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.
  61. Ying Wu & Richard J. Cook, 2018. "Variable selection and prediction in biased samples with censored outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 72-93, January.
  62. Yuan Jiang & Yunxiao He & Heping Zhang, 2016. "Variable Selection With Prior Information for Generalized Linear Models via the Prior LASSO Method," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 355-376, March.
  63. 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.
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