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Adaptive Lasso for Cox's proportional hazards model

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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. Toshio Honda & Wolfgang Karl Härdle, 2012. "Variable selection in Cox regression models with varying coefficients," SFB 649 Discussion Papers SFB649DP2012-061, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  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 1-22, 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. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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).
  48. Wenbin Lu & Lexin Li, 2011. "Sufficient Dimension Reduction for Censored Regressions," Biometrics, The International Biometric Society, vol. 67(2), pages 513-523, June.
  49. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.
  50. 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.
  51. 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.
  52. 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.
  53. 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.
  54. 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.
  55. 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.
  56. 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.
  57. 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.
  58. 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.
  59. 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.
  60. 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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