REFERENCES
- Akaike , H. 1974 . “New Look at the Statistical Model Identification,” . IEEE Transactions on Automatic Control , 19 : 716 – 723 .
- Barro , R. and Lee , J. 1994 . Data Set for a Panel of 138 Countries , Cambridge , MA : National Bureau of Economic Research .
- Bondell , H. , Reich , B. and Wang , H. 2010 . “Non-Crossing Quantile Regression Curve Estimation,” . Biometrika , 97 : 825 – 838 .
- Fan , J. and Li , R. 2001 . “Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties,” . Journal of the American Statistical Association , 96 : 1348 – 1360 .
- He , X. 1997 . “Quantile Curves Without Crossing,” . The American Statistician , 51 : 186 – 192 .
- Kato , K. 2010 . “Solving l 1 Regularization Problems With Piecewise Linear Losses,” . Journal of Computational and Graphical Statistics , 19 : 1024 – 1040 .
- Koenker , R. 2004 . “Quantile Regression for Longitudinal Data,” . Journal of Multivariate Analysis , 91 : 74 – 89 .
- ——— . 2005 . Quantile Regression , Cambridge : Cambridge University Press .
- Koenker , R. and Bassett , G. 1978 . “Regression Quantiles,” . Econometrica , 4 : 33 – 50 .
- Koenker , R. and Machado , J. 1999 . “Goodness of Fit and Related Inference Processes for Quantile Regression,” . Journal of the American Statistical Association , 94 : 1296 – 1310 .
- Li , Y. and Zhu , J. 2007 . “Analysis of Array CGH Data for Cancer Studies Using Fused Quantile Regression,” . Bioinformatics , 23 : 2470 – 2476 .
- ——— . 2008 . “L1-Norm Quantile Regression,” . Journal of Computational and Graphical Statistics , 17 : 163 – 185 .
- Osborne , M. R. and Turlach , B. A. 2011 . “A Homotopy Algorithm for the Quantile Regression Lasso and Related Piecewise Linear Problems,” . Journal of Computational and Graphical Statistics , 20 : 972 – 987 .
- Tackeuchi , I. , Le , Q. , Sears , T. and Smola , A. 2006 . “Nonparametric Quantile Estimation,” . Journal of Machine Learning Research , 7 : 1231 – 1264 .
- Tibshirani , R. 1996 . “Regression Shrinkage and Selection via the Lasso,” . Journal of the Royal Statistical Society, Series B , 58 : 267 – 288 .
- Tibshirani , R. , Saunders , M. , Rosset , S. , Zhu , J. and Knight , K. 2005 . “Sparsity and Smoothness via the Fused Lasso,” . Journal of the Royal Statistical Society, Series B , 67 : 91 – 108 .
- Wang , H. and Hu , J. 2011 . “Identification of Differential Aberrations in Multiple-Sample Array CGH Studies,” . Biometrics , 67 : 353 – 362 .
- Wu , Y. and Liu , Y. 2009a . “Variable Selection in Quantile Regression,” . Statistica Sinica , 19 : 801 – 817 .
- ——— . 2009b . “Stepwise Multiple Quantile Regression Estimation Using Non-Crossing Constraints,” . Statistica and Its Interface , 2 : 299 – 310 .
- 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 .
- Zhao , P. , Rocha , G. and Yu , B. 2009 . “The Composite Absolute Penalties Family for Grouped and Hierarchical Variable Selection,” . The Annals of Statistics , 37 : 3468 – 3497 .
- Zou , H. 2006 . “The Adaptive Lasso and Its Oracle Properties,” . Journal of the American Statistical Association , 101 : 1418 – 1429 .
- Zou , H. and Yuan , M. 2008a . “Composite Quantile Regression and the Oracle Model Selection Theory,” . The Annals of Statistics , 36 : 1108 – 1126 .
- ——— . 2008b . “Regularized Simultaneous Model Selection in Multiple Quantiles Regression,” . Computational Statistics and Data Analysis , 52 : 5296 – 5304 .