@article{zhang2010,
   title={Nearly unbiased variable selection under minimax concave penalty},
   volume={38},
   ISSN={0090-5364},
   number={2},
   journal={The Annals of Statistics},
   publisher={Institute of Mathematical Statistics},
   author={Zhang, Cun-Hui},
   year={2010},
   month={Apr}
}


@article{wangli2009,
author = {Wang, Hansheng and Li, Bo and Leng, Chenlei},
title = {Shrinkage tuning parameter selection with a diverging number of parameters},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
volume = {71},
number = {3},
pages = {671-683},
keywords = {Bayesian information criterion, Diverging number of parameters, Lasso, Smoothly clipped absolute deviation},
abstract = {Summary.  Contemporary statistical research frequently deals with problems involving a diverging number of parameters. For those problems, various shrinkage methods (e.g. the lasso and smoothly clipped absolute deviation) are found to be particularly useful for variable selection. Nevertheless, the desirable performances of those shrinkage methods heavily hinge on an appropriate selection of the tuning parameters. With a fixed predictor dimension, Wang and co-worker have demonstrated that the tuning parameters selected by a Bayesian information criterion type criterion can identify the true model consistently. In this work, similar results are further extended to the situation with a diverging number of parameters for both unpenalized and penalized estimators. Consequently, our theoretical results further enlarge not only the scope of applicabilityation criterion type criteria but also that of those shrinkage estimation methods.},
year = {2009}
}

