Hierarchically penalized quantile regression

References

• Cole T, Green P. Smoothing reference centile curves: the lms method and penalized likelihood. Stat Med. 1992;11:1305–1319. doi: 10.1002/sim.4780111005
   PubMed Web of Science ®Google Scholar
• Heagerty P, Pepe M. Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in U.S. children. J Roy Statist Soc Ser C. 1999;48:533–551. doi: 10.1111/1467-9876.00170
   Web of Science ®Google Scholar
• Hendricks W, Koenker R. Hierarchical spline models for conditional quantiles and the demand for electricity. J Amer Statist Assoc. 1992;87:58–68. doi: 10.1080/01621459.1992.10475175
   Web of Science ®Google Scholar
• Koenker R, Hallock K. Quantile regression. J Econ Perspect. 2001;15:143–156. doi: 10.1257/jep.15.4.143
   Web of Science ®Google Scholar
• Yang S. Censored median regression using weighted empirical survival and hazard functions. J Amer Statist Assoc. 1999;94:37–145.
   Google Scholar
• Koenker R, Geling R. Reappraising medfly longevity: a quantile regression survival analysis. J Amer Statist Assoc. 2001;96:458–468. doi: 10.1198/016214501753168172
   Web of Science ®Google Scholar
• Wei Y, Pere A, Koenker R, He X. Quantile regression methods for reference growth curves. Stat Med. 2006;25:1369–1382. doi: 10.1002/sim.2271
   PubMed Web of Science ®Google Scholar
• Wei Y, He X. Conditional growth charts (with discussions). Ann Statist. 2006;34:2069–2031. doi: 10.1214/009053606000000623
   Web of Science ®Google Scholar
• Wang H, He X. Detecting differential expressions in genechip microarray studies: a quantile approach. J Amer Statist Assoc. 2007;102:104–112. doi: 10.1198/016214506000001220
   Web of Science ®Google Scholar
• Koenker R, Bassett G. Regression quantiles. Econometrica. 1978;46:33–50. doi: 10.2307/1913643
   Web of Science ®Google Scholar
• Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Amer Statist Assoc. 2001;96:1348–1360. doi: 10.1198/016214501753382273
   Web of Science ®Google Scholar
• Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B. 1996;58:267–288.
   Google Scholar
• Zou H. The adaptive lasso and its oracle properties. J Amer Statist Assoc. 2006;101:1418–1429. doi: 10.1198/016214506000000735
   Web of Science ®Google Scholar
• Koenker R. Quantile regression for longitudinal data. J Multivariate Anal. 2004;91:74–89. doi: 10.1016/j.jmva.2004.05.006
   Web of Science ®Google Scholar
• Li Y, Zhu J. L1-norm quantile regression. J Comput Graph Statist. 2008;17:1–23. doi: 10.1198/106186008X289155
   Web of Science ®Google Scholar
• Wu Y, Liu Y. Variable selection in quantile regression. Statist Sinica. 2009;19:801–817.
   Web of Science ®Google Scholar
• Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B. 2006;68:49–67. doi: 10.1111/j.1467-9868.2005.00532.x
   Google Scholar
• Wang H, Leng C. A note on adaptive group lasso. Comput Statist Data Anal. 2008;68:49–67.
   Google Scholar
• Bang S, Jhun M. Simultaneous estimation and factor selection in quantile regression via adaptive sup-norm regularization. Comput Statist Data Anal. 2012;56:813–826. doi: 10.1016/j.csda.2011.01.026
   Web of Science ®Google Scholar
• Huang J, Ma S, Xie H, Zhang C. A group bridge approach for variable selection. Biometrika. 2009;96:339–355. doi: 10.1093/biomet/asp020
   PubMed Web of Science ®Google Scholar
• Wang S, Nan B, Zhou N, Zhu J. Hierarchically penalized Cox regression with grouped variables. Biometrika. 2009;96:307–322. doi: 10.1093/biomet/asp016
   Web of Science ®Google Scholar
• Knight K. Asymptotics for L1-estimators of regression parameters under heteroscedasticity. Canad J Statist. 1999;27:497–507. doi: 10.2307/3316107
   Web of Science ®Google Scholar
• Koenker R. Quantile regression. New York: Cambridge University Press; 2005.
   Google Scholar
• Schwarz G. Estimating the dimension of a model. Ann Statist. 1978;6:461–464. doi: 10.1214/aos/1176344136
   Web of Science ®Google Scholar
• Hosmer DW, Lemeshow S. Applied logistic regression. New York: Wiley; 2000.
   Google Scholar
• Knight K. Limiting distributions for L1 regression estimators under general conditions. Ann Statist. 1998;26:755–770. doi: 10.1214/aos/1028144858
   Web of Science ®Google Scholar