Simultaneous Estimation and Variable Selection for Interval-Censored Data With Broken Adaptive Ridge Regression

References

• Bradic, J., Fan, J., and Jiang, J. (2011), “Regularization for Cox’s Proportional Hazards Model With NP-Dimensionality,” The Annals of Statistics, 39, 3092–3120. DOI:10.1214/11-AOS911.
   PubMed Web of Science ®Google Scholar
• Dicker, L., Huang, B., and Lin, X. (2013), “Variable Selection and Estimation With the Seamless-L0 Penalty,” Statistica Sinica, 23, 929–962.
   Web of Science ®Google Scholar
• Fan, J., and Li, R. (2001), “Variable Selection Via Nonconcave Penalized Likelihood and Its Oracle Property,” Journal of the American Statistical Association, 96, 1348–1360. DOI:10.1198/016214501753382273.
   Web of Science ®Google Scholar
• Fan, J., and Li, R. (2002), “Variable Selection for Cox’s Proportional Hazards Model and Frailty Model,” The Annals of Statistics, 30, 74–99. DOI:10.1214/aos/1015362185.
   Web of Science ®Google Scholar
• Finkelstein, D. M. (1986), “A Proportional Hazards Model for Interval-Censored Failure Time Data,” Biometrics, 42, 845–854.
   PubMed Web of Science ®Google Scholar
• Huang, J., and Ma, S. (2010), “Variable Selection in the Accelerated Failure Time Model Via the Bridge Method,” Lifetime Data Analysis, 16, 176–195. DOI:10.1007/s10985-009-9144-2.
   PubMed Web of Science ®Google Scholar
• Jewell, N. P., and van der Laan, M. (2004), “Case-Control Current Status Data,” Biometrika, 91, 529–541. DOI:10.1093/biomet/91.3.529.
   Web of Science ®Google Scholar
• Kawaguchi, E., Suchard, M., Liu, Z. and Li, G. (2017), “Scalable Sparse Cox’s Regression for Large-Scale Survival Data Via Broken Adaptive Ridge,” arXiv no. 1712.00561 [stat.ME].
   Google Scholar
• Kneib, T. (2006), “Mixed Model-Based Inference in Geoadditive Hazard Regression for Interval-Censored Survival Times,” Computational Statistics and Data Analysis, 51, 777–792. DOI:10.1016/j.csda.2006.06.019.
   Web of Science ®Google Scholar
• Liu, Z., and Li, G. (2016), “Efficient Regularized Regression With L0 Penalty for Variable Selection and Network Construction,” Computational and Mathematical Methods in Medicine, Article ID 3456153. DOI:10.1155/2016/3456153.
   Web of Science ®Google Scholar
• Lv, J., and Fan, Y. (2009), “A Unified Approach to Model Selection and Sparse Recovery Using Regularized Least Squares,” The Annals of Statistics, 37, 3498–3528. DOI:10.1214/09-AOS683.
   Web of Science ®Google Scholar
• Ma, L., Hu, T., and Sun, J. (2015), “Sieve Maximum Likelihood Regression Analysis of Dependent Current Status Data,” Biometrika, 102, 731–738. DOI:10.1093/biomet/asv020.
   Web of Science ®Google Scholar
• Scolas, S., El Ghouch, A., Legrand, C., and Oulhaj, A. (2016), “Variable Selection in a Flexible Parametric Mixture Cure Model With Interval-Censored Data,” Statistics in Medicine, 35, 1210–1225. DOI:10.1002/sim.6767.
   PubMed Web of Science ®Google Scholar
• Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., and Friedman, N. (2003), “Module Networks: Identifying Regulatory Modules and Their Condition-Specific Regulators From Gene Expression Data,” Nature Genetics, 34, 166–176. DOI:10.1038/ng1165.
   PubMed Web of Science ®Google Scholar
• Shi, Y., Cao, Y., Jiao Y., and Liu Y. (2014), “SICA for Cox’s Proportional Hazards Model With a Diverging Number of Parameters,” Acta Mathematicae Applicatae Sinica, English Series, 30, 887–902. DOI:10.1007/s10255-014-0402-z.
   Web of Science ®Google Scholar
• Sun, J. (2006), The Statistical Analysis of Interval-Censored Failure Time Data, New York: Springer.
   Google Scholar
• Sun, J., Feng, Y., and Zhao, H. (2015), “Simple Estimation Procedures for Regression Analysis of Interval-Censored Failure Time Data Under the Proportional Hazards Model,” Lifetime Data Analysis, 21, 138–155. DOI:10.1007/s10985-013-9282-4.
   PubMed Web of Science ®Google Scholar
• Tibshirani, R. (1996), “Regression Shrinkage and Selection Via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288. DOI:10.1111/j.2517-6161.1996.tb02080.x.
   Google Scholar
• Tibshirani, R. (1997), “The Lasso Method for Variable Selection in the Cox Model,” Statistics in Medicine, 16, 385–95.
   PubMed Web of Science ®Google Scholar
• Wang, H., and Leng, C. (2007), “Unified LASSO Estimation by Least Squares Approximation,” Journal of the American Statistical Association, 102, 1039–1048. DOI:10.1198/016214507000000509.
   Web of Science ®Google Scholar
• Wang, J., and Ghosh, S. K. (2012), “Shape Restricted Nonparametric Regression With Bernstein Polynomials,” Computational Statistics and Data Analysis, 56, 2729–2741. DOI:10.1016/j.csda.2012.02.018.
   Web of Science ®Google Scholar
• Wu, Y., and Cook, R. (2015), “Penalized Regression for Interval-Censored Times of Disease Progression: Selection of HLA Markers in Psoriatic Arthritis,” Biometrics, 71, 782–791. DOI:10.1111/biom.12302.
   PubMed Web of Science ®Google Scholar
• Yuan, M., and Lin, Y. (2006), “Model Selection and Estimation in Regression With Grouped Variables,” Journal of the Royal Statistical Society, Series B, 68, 49–67. DOI:10.1111/j.1467-9868.2005.00532.x.
   Google Scholar
• Zhang, C. H. (2010), “Nearly Unbiased Variable Selection Under Minimax Concave Penalty,” The Annals of Statistics, 38, 894–942. DOI:10.1214/09-AOS729.
   Web of Science ®Google Scholar
• Zhang, H., and Lu, W. B. (2007), “Adaptive Lasso for Cox’s Proportional Hazards Model,” Biometrika, 94, 1–13.
   Web of Science ®Google Scholar
• Zhang, Y., Hua, L., and Huang, J. (2010), “A Spline-Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model With Interval-Censored Data,” Scandinavian Journal of Statistics, 37, 338–354. DOI:10.1111/j.1467-9469.2009.00680.x.
   Web of Science ®Google Scholar
• Zhou, Q., Hu, T., and Sun, J. (2017), “A Sieve Semiparametric Maximum Likelihood Approach for Regression Analysis of Bivariate Interval-Censored Failure Time Data,” Journal of the American Statistical Association, 112, 664–672. DOI:10.1080/01621459.2016.1158113.
   Web of Science ®Google Scholar
• Zou, H. (2006), “The Adaptive Lasso and Its Oracle Properties,” Journal of the American Statistical Association, 101, 1418–1429. DOI:10.1198/016214506000000735.
   Web of Science ®Google Scholar
• Zou, H., Hastie, T. (2005), “Regularization and Variable Selection Via the Elastic Net,” Journal of the Royal Statistical Society, Series B, 67, 301–320. DOI:10.1111/j.1467-9868.2005.00503.x.
   Google Scholar