User menu

Variable selection in proportional hazards cure model with time-varying covariates, application to US bank failures

  1. Boag J.W., J. R. Stat. Soc. Ser. B. Stat. Methodol., 11, 15 (1949)
  2. Breiman Leo, Heuristics of instability and stabilization in model selection, 10.1214/aos/1032181158
  3. Breslow N.E., J. R. Stat. Soc. Ser. B. Stat. Methodol., 34, 216 (1972)
  4. Cantor Alan B., Shuster Jonathan J., Parametric versus non-parametric methods for estimating cure rates based on censored survival data, 10.1002/sim.4780110710
  5. Cole Rebel A., Gunther Jeffery W., Separating the likelihood and timing of bank failure, 10.1016/0378-4266(95)98952-m
  6. De Leonardis Daniele, Rocci Roberto, Default risk analysis via a discrete-time cure rate model, 10.1002/asmb.1998
  7. Demirgüç-Kunt A., Fed. Res. Bank Cleve. Econ. Rev., 25, 2 (1989)
  8. Demyanyk Yuliya, Hasan Iftekhar, Financial crises and bank failures: A review of prediction methods, 10.1016/j.omega.2009.09.007
  9. Denham J.W., Denham E., Dear K.B., Hudson G.V., The follicular non-Hodgkin's Lymphomas—I. The possibility of cure, 10.1016/0959-8049(95)00607-9
  10. Dirick Lore, Bellotti Tony, Claeskens Gerda, Baesens Bart, Macro-Economic Factors in Credit Risk Calculations: Including Time-Varying Covariates in Mixture Cure Models, 10.1080/07350015.2016.1260471
  11. Dirick Lore, Claeskens Gerda, Baesens Bart, An Akaike information criterion for multiple event mixture cure models, 10.1016/j.ejor.2014.08.038
  12. Fan Jianqing, Li Runze, Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties, 10.1198/016214501753382273
  13. Li Runze, Fan Jianqing, Frailty Model, 10.1214/aos/1015362185
  14. Fan Jianqing, Li Runze, New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis, 10.1198/016214504000001060
  15. Peng Heng, Fan Jianqing, Nonconcave penalized likelihood with a diverging number of parameters, 10.1214/009053604000000256
  16. Farewell V. T., The Use of Mixture Models for the Analysis of Survival Data with Long-Term Survivors, 10.2307/2529885
  17. Farewell Vernon T., Mixture models in survival analysis: Are they worth the risk?, 10.2307/3314804
  18. Ghitany M.E., Maller R.A., Zhou S., Exponential Mixture Models with Long-Term Survivors and Covariates, 10.1006/jmva.1994.1023
  19. Hendry David J., Data generation for the Cox proportional hazards model with time-dependent covariates: a method for medical researchers, 10.1002/sim.5945
  20. Hoerl Arthur E., Kennard Robert W., Ridge Regression: Applications to Nonorthogonal Problems, 10.1080/00401706.1970.10488635
  21. Hunter David R., Li Runze, Variable selection using MM algorithms, 10.1214/009053605000000200
  22. Jones D., Biom. Praximetrie, 21, 1 (1981)
  23. Kalbfleisch John D., Prentice Ross L., The Statistical Analysis of Failure Time Data : Kalbfleisch/The Statistical, ISBN:9781118032985, 10.1002/9781118032985
  24. KUK ANTHONY Y. C., CHEN CHEN-HSIN, A mixture model combining logistic regression with proportional hazards regression, 10.1093/biomet/79.3.531
  25. Lane William R., Looney Stephen W., Wansley James W., An application of the cox proportional hazards model to bank failure, 10.1016/s0378-4266(86)80003-6
  26. Pötscher Benedikt M., Leeb Hannes, On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding, 10.1016/j.jmva.2009.06.010
  27. Liu Fan, Hua Zhongsheng, Lim Andrew, Identifying future defaulters: A hierarchical Bayesian method, 10.1016/j.ejor.2014.08.008
  28. Louis Thomas A., Finding the Observed Information Matrix When Using the EM Algorithm, 10.1111/j.2517-6161.1982.tb01203.x
  29. Mallows C. L., Some Comments onCp, 10.1080/00401706.1973.10489103
  30. McLachlan Geoffrey J., Krishnan Thriyambakam, The EM Algorithm and Extensions, 2E, ISBN:9780470191613, 10.1002/9780470191613
  31. Peng Yingwei, Dear Keith B. G., A Nonparametric Mixture Model for Cure Rate Estimation, 10.1111/j.0006-341x.2000.00237.x
  32. Pfeiffer Ruth M., Redd Andrew, Carroll Raymond J., On the impact of model selection on predictor identification and parameter inference, 10.1007/s00180-016-0690-2
  33. Sanderson Conrad, Curtin Ryan, Armadillo: a template-based C++ library for linear algebra, 10.21105/joss.00026
  34. Schwarz Gideon, Estimating the Dimension of a Model, 10.1214/aos/1176344136
  35. Shi Haolun, Yin Guosheng, Landmark cure rate models with time-dependent covariates, 10.1177/0962280217708681
  36. Sy Judy P., Taylor Jeremy M. G., Estimation in a Cox Proportional Hazards Cure Model, 10.1111/j.0006-341x.2000.00227.x
  37. Sy J.P., Taylor J.M.G., Standard errors for the cox proportional hazards cure model, 10.1016/s0895-7177(00)00312-5
  38. Thomson J.B., Fed. Res. Bank Cleve. Econ. Rev., 27, 9 (1991)
  39. Tibshirani Robert, Regression Shrinkage and Selection Via the Lasso, 10.1111/j.2517-6161.1996.tb02080.x
  40. Tong Edward N.C., Mues Christophe, Thomas Lyn C., Mixture cure models in credit scoring: If and when borrowers default, 10.1016/j.ejor.2011.10.007
  41. Whalen G., Fed. Res. Bank Cleve. Econ. Rev., 27, 21 (1991)
  42. Wheelock David C., Wilson Paul W., Why do Banks Disappear? The Determinants of U.S. Bank Failures and Acquisitions, 10.1162/003465300558560
  43. Zhang Yiyun, Li Runze, Tsai Chih-Ling, Regularization Parameter Selections via Generalized Information Criterion, 10.1198/jasa.2009.tm08013
  44. Zou Hui, The Adaptive Lasso and Its Oracle Properties, 10.1198/016214506000000735
Bibliographic reference Beretta, Alessandro ; Heuchenne, Cédric. Variable selection in proportional hazards cure model with time-varying covariates, application to US bank failures. In: Journal of Applied Statistics, Vol. 46, no. 9, p. 1529-1549 (2019)
Permanent URL http://hdl.handle.net/2078.1/208979