A high dimensional BIC will be returned specificall for quantile regression
1 
y 
response 
X 

beta 
the coefficients vector for BIC calculation 
tau 

const 
a constant to adjust the BIC. A positive numerical value; default value is 6. 
The high dimensional BIC for quantile regression model is
log(checkloss)+Slog(log(n))C_n/n
where S is the selected model in QICD, n is the number of obs, C_n is some positive constant which diverges to infinity as n increases. Actually, C_n is log(p)/const
.
QBIC will be returned, which is a numerical value
Bo Peng
Lee, E. R., Noh, H. and Park. B. (2013) Model Selection via Bayesian Information Criterion for Quantile Regression Models. Journal of the American Statistical Associa tion, preprint. http://www.tandfonline.com/doi/pdf/10.1080/01621459.2013.836975 doi: 10.1080/01621459.2013.836975
Wang,L., Kim, Y., and Li,R. (2013+) Calibrating nonconvex penalized regression in ultrahigh dimension. To appear in Annals of Statistics. http://users.stat.umn.edu/~wangx346/research/nonconvex.pdf
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