|
Breheny, P. and Huang, J. (2015). Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors. Statistics and Computing 25, 173–187. Breiman, L. (1996). Heuristics of instability and stabilization in model selection. The Annals of Statistics 24, 2350–2383. Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society. Series B 34, 187–220. Cox, D. R. (1975). Partial likelihood. Biometrika 62, 269–276. Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004). Least angle regression. The Annals of Statistics 32, 407–499. Fan, J. and Li, R. (2002). Variable selection for cox’s proportional hazards model and frailty model. The Annals of Statistics 30, 74–99. Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33, 1–22. Kalbfleisch, J. D. and Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data. John Wiley & Sons, 2nd edition. Kaplan, E. L. and Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53, 457–481. Lu, W. and Zhang, H. H. (2007). Variable selection for proportional odds model. Statistics in Medicine 26, 3771–3781. Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine 127, 757–763. Simon, N., Friedman, J., Hastie, T., Tibshirani, R., et al. (2011). Regularization paths for cox’s proportional hazards model via coordinate descent. Journal of Statistical Software 39, 1–13. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58, 267–288. Tibshirani, R. et al. (1997). The lasso method for variable selection in the cox model. Statistics in Medicine 16, 385–395. Tsai, W.-Y., Jewell, N. P., and Wang, M.-C. (1987). A note on the product-limit estimator under right censoring and left truncation. Biometrika 74, 883–886. van Houwelingen, H. C., Bruinsma, T., Hart, A. A. M., van’t Veer, L. J., and Wessels, L. F. A. (2006). Cross-validated cox regression on microarray gene expression data. Statistics in Medicine 25, 3201–3216. Wang, H. and Leng, C. (2008). A note on adaptive group lasso. Computational Statistics and Data Analysis 52, 5277–5286. Wang, L., Chen, G., and Li, H. (2007). Group scad regression analysis for microarray time course gene expression data. Bioinformatics 23, 1486–1494. Wang, M.-C. (1989). A semiparametric model for randomly truncated data. Journal of the American Statistical Association 84, 742–748. Wang, M.-C., Brookmeyer, R., and Jewell, N. P. (1993). Statistical models for prevalent cohort data. Biometrics 49, 1–11. Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68, 49–67. Zhang, C.-H. (2010). Nearly unbiased variable selection under minimax concave penalty. The Annals of Statistics 38, 894–942. Zhang, H. H. and Lu, W. (2007). Adaptive lasso for cox’s proportional hazards model. Biometrika 94, 691–703. Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101, 1418–1429. Zou, H. (2008). A note on path-based variable selection in the penalized proportional hazards model. Biometrika 95, 241–247. Zou, H. and Li, R. (2008). One-step sparse estimates in nonconcave penalized likelihood models. Annals of Statistics 36, 1509. |