[1] Engle, R.F., Granger, C.W., Rice, J. and Weiss, A. (1986) Semiparametric Estimates of the Relation between Weather and Electricity Sales. Journal of the American Statistical Association, 81, 310-320.
http://dx.doi.org/10.1080/01621459.1986.10478274
[2] Fan, J.Q. and Li, R.Z. (2004) New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis. Journal of the American Statistical Association, 99, 710-723.
http://dx.doi.org/10.1198/016214504000001060
[3] Bunea, F. (2004) Consistent Covariate Selection and Post Model Selection Inference in Semi-Parametric Regression. The Annals of Statistics, 32, 898-927.
http://dx.doi.org/10.1214/009053604000000247
[4] Li, R.Z. and Liang, H. (2008) Variable Selection in Semiparametric Regression Modeling. The Annals of Statistics, 36, 261-286.
http://dx.doi.org/10.1214/009053607000000604
[5] Liang, H. and Li., R.Z. (2009) Variable Selection for Partially Linear Models with Measurement Errors. Journal of the American Statistical Association, 104, 234-248.
http://dx.doi.org/10.1198/jasa.2009.0127
[6] Chen, B.C., Yu, Y., Zou, H. and Liang, H. (2012) Profiled Adaptive Elastic-Net Procedure for Partially Linear Models with High-Dimensional Covariates. Journal of Statistical Planning and Inference, 142, 1733-1745.
http://dx.doi.org/10.1016/j.jspi.2012.02.035
[7] Zou, H. and Zhang, H.H. (2009) On the Adaptive Elastic-Net with a Diverging Number of Parameters. The Annals of Statistics, 37, 1733-1751.
http://dx.doi.org/10.1214/08-AOS625
[8] Xie, H.L. and Huang, J. (2009) SCAD-Penalized Regression in High-Dimensional Partially Linear Models. The Annals of Statistics, 37, 673-696.
http://dx.doi.org/10.1214/07-AOS580
[9] Fan, J.Q. and Li, R.Z. (2001) Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties. Journal of the American Statistical Association, 96, 1348-1360.
http://dx.doi.org/10.1198/016214501753382273
[10] Ni, X., Zhang, H.H. and Zhang, D.W. (2009) Automatic Model Selection for Partially Linear Models. Journal of multivariate Analysis, 100, 2100-2111.
http://dx.doi.org/10.1016/j.jmva.2009.06.009
[11] Koenker, R. (2005) Quantile Regression. Cambridge University Press, Cambridge.
http://dx.doi.org/10.1017/CBO9780511754098
[12] Green, P.J. and Silverman, B.W. (1994) Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach. Chapman & Hall, London.
[13] Hunter, D.R. and Lange, K. (2004) A Tutorial on MM Algorithms. The American Statistician, 58, 30-37.
http://dx.doi.org/10.1198/0003130042836
[14] Hunter, D.R. and Lange, K. (2000) Quantile Regression via an MM Algorithm. Journal of Computational and Graphical Statistics, 9, 60-77.
http://dx.doi.org/10.2307/1390613
[15] Zou, H. and Li, R.Z. (2008) One-Step Sparse Estimates in Nonconcave Penalized Likelihood Models. The Annals of Statistics, 36, 1509-1533.
http://dx.doi.org/10.1214/009053607000000802
[16] Chen, J.H. and Chen, Z.H. (2008) Extended Bayesian Information Criteria for Model Selection with Large Model Spaces. Biometrika, 95, 759-771.
http://dx.doi.org/10.1093/biomet/asn034
[17] Ruppert, D., Wand, M.P. and Carroll, R. J. (2003) Semiparametric Regression. Cambridge University Press, Cambridge.
http://dx.doi.org/10.1017/CBO9780511755453