References
- Koenker R, Bassett GW. Regression quantiles. Econometrica. 1978;46:33–50. doi: 10.2307/1913643
- Zou H, Yuan M. Composite quantile regression and the oracle model selection theory. Ann Stat. 2008;36:1108–1126. doi: 10.1214/07-AOS507
- Zhao Z, Xiao Z. Efficient regressions via optimally combining quantile information. Econ Theory. 2014;30:1272–1314. doi: 10.1017/S0266466614000176
- Bradic J, Fan J, Wang W. Penalized composite quasi-likelihood for ultrahigh dimensional variable selection. J R Stat Soc: Ser B (Stat Methodol). 2011;73:325–349. doi: 10.1111/j.1467-9868.2010.00764.x
- Kottas A, Gelfand AE. Bayesian semiparametric median regression modeling. J Am Stat Assoc. 2001;96:1458–1468. doi: 10.1198/016214501753382363
- Kozumi A, Kobayashi G. Gibbs sampling methods for Bayesian quantile regression. J Stat Comput Simul. 2011;81:1565–1578. doi: 10.1080/00949655.2010.496117
- Tsionas EG. Bayesian quantile inference. J Stat Comput Simul. 2003;73:659–674. doi: 10.1080/0094965031000064463
- Walker S, Mallick BK. A Bayesian semiparametric accelerated failure time model. Biometrics. 1999;55:477–483. doi: 10.1111/j.0006-341X.1999.00477.x
- Yu K, Chen CWS, Reed C, Dunson DB. Bayesian variable selection in quantile regression. Stat Interface. 2013;6:261–274. doi: 10.4310/SII.2013.v6.n2.a9
- Yu K, Moyeed RA. Bayesian quantile regression. Stat Probab Lett. 2001;54:437–447. doi: 10.1016/S0167-7152(01)00124-9
- Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc: Ser B. 1996;58(1):267–288.
- Koenker R. Quantile regression for longitudinal data. J Multivariate Anal. 2004;91:74–89. doi: 10.1016/j.jmva.2004.05.006
- Li Y, Zhu J. l1-Norm quantile regression. J Comput Graph Stat. 2004;17:163–185. doi: 10.1198/106186008X289155
- Wang H, Li G, Jiang G. Robust regression shrinkage and consistent variable selection through the lad-lasso. J Bus Econ Stat. 2007;25:347–355. doi: 10.1198/073500106000000251
- Li Q, Lin N, Xi R. Bayesian regularized quantile regression. Bayesian Anal. 2010;5:429–618. doi: 10.1214/10-BA517
- Alhamzawi R, Yu K, Benoit DF. Bayesian adaptive lasso quantile regression. Stat Model. 2013;13:223–252. doi: 10.1177/1471082X13480650
- Andrews DF, Mallows CL. Scale mixtures of normal distributions. J R Stat Soc: Ser B. 1974;36:99–102.
- Fan J, Li R. Variable selection via nonconcave penalized likelihood and it oracle properties. J Am Stat Assoc. 2001;96:1348–1360. doi: 10.1198/016214501753382273
- Harrison D, Rubinfeld DL. Hedonic housing prices and the demand for clean air. J Environ Econ Manage. 1978;5:81–102. doi: 10.1016/0095-0696(78)90006-2
- R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2013. Available from: http://www.R-project.org/http://www.R-project.org/
- Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc: Ser B (Stat Methodol). 2002;64(4):583–639. doi: 10.1111/1467-9868.00353
- Yuan M, Lin Y. Model selection and estimation in the Gaussian graphical model. Biometrika. 2007;94(1):19–35. doi: 10.1093/biomet/asm018
- Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc: Ser B (Stat Methodol). 2005;67:301–320. doi: 10.1111/j.1467-9868.2005.00503.x
- Tibshirani R, Saunders M, Rosset S, Zhu J, Knight K. Sparsity and smoothness via the fused lasso. J R Stat Soc: Ser B (Stat Methodol). 2005;67:91–108. doi: 10.1111/j.1467-9868.2005.00490.x